Cellchat script
Amanda Oliver
15/05/23
Preprocessing in python: briefly I normalised and log transformed the raw counts data, filtered for celltypes/conditions of interest and export the normalised counts as csv (same as cellphoneDB), also export metadata for cell type (level_3_annot).
Install CellChat (https://github.com/sqjin/CellChat)
#devtools::install_github("jokergoo/ComplexHeatmap")
#devtools::install_github("jokergoo/circlize")
#devtools::install_github("sqjin/CellChat")Install other dependencies
#install.packages('NMF')Load required libraries
library(CellChat)
library(patchwork)
library(Seurat)
library(SeuratObject)
library(plyr)
options(stringsAsFactors = FALSE)
library(NMF)
library(dplyr)
#library(sceasy)
library(igraph)
library(Matrix)
library(ggplot2)
library(ggalluvial)
library(circlize)Load input data and meta data
data <- read.table(file = '/nfs/team205/ao15/Megagut/Annotations_v3/disease_analysis/interactions/counts/pooled_disease.remapped.allgenes.AP_SI.counts.csv', header = T, row.names=1, sep=",", as.is=T)
head(data[,1:10])meta <- read.csv(file = '/nfs/team205/ao15/Megagut/Annotations_v3/disease_analysis/interactions/meta/pooled_disease.remapped.allgenes.AP_SI.meta.csv', header = T, row.names=1,sep=",", as.is=T)
head(data[,1:10])head(meta)#to make the barcodes in counts consistent, for some reason the barcodes that read "AACCGCGCAGTTTACG-HCA_A_GT12934998" are being changed to "AACCGCGCAGTTTACG.HCA_A_GT12934998"
colnames(data) = gsub('[.]', '-', colnames(data))
#head(data[,1:10])
#quickly list barcodes by transposing dataframe
#data_t <- t(data)
#row.names(data_t)
head(data[,1:10])Make seurat object from csv
srat <- CreateSeuratObject(data, project = "MUC6", assay = "RNA", min.cells = 0, min.features = 0, meta.data = meta)From here the script follows the CellChat tutorial
data.input <- srat@assays$RNA@counts
cell.use = rownames(meta)Create a cellchat object:
cellchat <- createCellChat(object = data.input, meta = meta, group.by = "level_3_annot")Create a CellChat object from a data matrix
Set cell identities for the new CellChat objectThe cell groups used for CellChat analysis are B_GC_I B_GC_II B_memory B_naive B_plasma_IgA1 B_plasma_IgA2 B_plasma_IgG B_plasma_IgM B_plasmablast BEST4_enterocyte_colonocyte Crypt_fibroblast_PI16 DC_cDC1 DC_cDC2 DC_langerhans DC_migratory DC_pDC EC_arterial_1 EC_arterial_2 EC_capillary EC_cycling EC_lymphatic EC_venous Enterocyte Enteroendocrine Eosinophil/basophil Epithelial_stem Erythrocytes Fibroblast_reticular Follicular_DC gdT gdT_naive Glial_1 Glial_2 Glial/Enteric_neural_crest Goblet Goblet_cycling Goblet_progenitor ILC3 Immune_recruiting_pericyte Lamina_propria_fibroblast_ADAMDEC1 Macrophage Macrophage_LYVE1 Macrophage_MMP9 Macrophage_TREM2 MAIT Mast Microfold Monocyte Mucous_gland_neck Myofibroblast Neuroblast NK_CD16 NK_CD56bright Oesophagus_fibroblast Oral_mucosa_fibroblast Paneth Pericyte Rectum_fibroblast Surface_foveolar T/NK_cycling TA Tfh Tfh_naive Tnaive/cm_CD4 Tnaive/cm_CD8 Treg Treg_IL10 Trm_CD4 Trm_CD8 Trm_Th17 Trm/em_CD8 Tuft Vascular_smooth_muscle Villus_fibroblast_F3 cellchat <- addMeta(cellchat, meta = meta)
cellchat <- setIdent(cellchat, ident.use = "level_3_annot") # set "labels" as default cell identity
levels(cellchat@idents) # show factor levels of the cell labels [1] "B_GC_I" "B_GC_II"
[3] "B_memory" "B_naive"
[5] "B_plasma_IgA1" "B_plasma_IgA2"
[7] "B_plasma_IgG" "B_plasma_IgM"
[9] "B_plasmablast" "BEST4_enterocyte_colonocyte"
[11] "Crypt_fibroblast_PI16" "DC_cDC1"
[13] "DC_cDC2" "DC_langerhans"
[15] "DC_migratory" "DC_pDC"
[17] "EC_arterial_1" "EC_arterial_2"
[19] "EC_capillary" "EC_cycling"
[21] "EC_lymphatic" "EC_venous"
[23] "Enterocyte" "Enteroendocrine"
[25] "Eosinophil/basophil" "Epithelial_stem"
[27] "Erythrocytes" "Fibroblast_reticular"
[29] "Follicular_DC" "gdT"
[31] "gdT_naive" "Glial_1"
[33] "Glial_2" "Glial/Enteric_neural_crest"
[35] "Goblet" "Goblet_cycling"
[37] "Goblet_progenitor" "ILC3"
[39] "Immune_recruiting_pericyte" "Lamina_propria_fibroblast_ADAMDEC1"
[41] "Macrophage" "Macrophage_LYVE1"
[43] "Macrophage_MMP9" "Macrophage_TREM2"
[45] "MAIT" "Mast"
[47] "Microfold" "Monocyte"
[49] "Mucous_gland_neck" "Myofibroblast"
[51] "Neuroblast" "NK_CD16"
[53] "NK_CD56bright" "Oesophagus_fibroblast"
[55] "Oral_mucosa_fibroblast" "Paneth"
[57] "Pericyte" "Rectum_fibroblast"
[59] "Surface_foveolar" "T/NK_cycling"
[61] "TA" "Tfh"
[63] "Tfh_naive" "Tnaive/cm_CD4"
[65] "Tnaive/cm_CD8" "Treg"
[67] "Treg_IL10" "Trm_CD4"
[69] "Trm_CD8" "Trm_Th17"
[71] "Trm/em_CD8" "Tuft"
[73] "Vascular_smooth_muscle" "Villus_fibroblast_F3" groupSize <- as.numeric(table(cellchat@idents)) # number of cells in each cell groupCellChatDB <- CellChatDB.human # use CellChatDB.mouse if running on mouse data
showDatabaseCategory(CellChatDB)
# Show the structure of the database
dplyr::glimpse(CellChatDB$interaction)Registered S3 method overwritten by 'cli':
method from
print.boxx spatstat.geomRows: 1,939
Columns: 11
$ interaction_name [3m[38;5;246m<chr>[39m[23m "TGFB1_TGFBR1_TGFBR2", "TGFB2_TGFBR1_TGFBR2", "TGFB3_TGFBR1_TGFBR2", "TGFB1…
$ pathway_name [3m[38;5;246m<chr>[39m[23m "TGFb", "TGFb", "TGFb", "TGFb", "TGFb", "TGFb", "TGFb", "TGFb", "TGFb", "TG…
$ ligand [3m[38;5;246m<chr>[39m[23m "TGFB1", "TGFB2", "TGFB3", "TGFB1", "TGFB1", "TGFB2", "TGFB2", "TGFB3", "TG…
$ receptor [3m[38;5;246m<chr>[39m[23m "TGFbR1_R2", "TGFbR1_R2", "TGFbR1_R2", "ACVR1B_TGFbR2", "ACVR1C_TGFbR2", "A…
$ agonist [3m[38;5;246m<chr>[39m[23m "TGFb agonist", "TGFb agonist", "TGFb agonist", "TGFb agonist", "TGFb agoni…
$ antagonist [3m[38;5;246m<chr>[39m[23m "TGFb antagonist", "TGFb antagonist", "TGFb antagonist", "TGFb antagonist",…
$ co_A_receptor [3m[38;5;246m<chr>[39m[23m "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "",…
$ co_I_receptor [3m[38;5;246m<chr>[39m[23m "TGFb inhibition receptor", "TGFb inhibition receptor", "TGFb inhibition re…
$ evidence [3m[38;5;246m<chr>[39m[23m "KEGG: hsa04350", "KEGG: hsa04350", "KEGG: hsa04350", "PMID: 27449815", "PM…
$ annotation [3m[38;5;246m<chr>[39m[23m "Secreted Signaling", "Secreted Signaling", "Secreted Signaling", "Secreted…
$ interaction_name_2 [3m[38;5;246m<chr>[39m[23m "TGFB1 - (TGFBR1+TGFBR2)", "TGFB2 - (TGFBR1+TGFBR2)", "TGFB3 - (TGFBR1+TGFB…#> Rows: 1,939
#> Columns: 11
#> $ interaction_name <chr> "TGFB1_TGFBR1_TGFBR2", "TGFB2_TGFBR1_TGFBR2", "TGF…
#> $ pathway_name <chr> "TGFb", "TGFb", "TGFb", "TGFb", "TGFb", "TGFb", "T…
#> $ ligand <chr> "TGFB1", "TGFB2", "TGFB3", "TGFB1", "TGFB1", "TGFB…
#> $ receptor <chr> "TGFbR1_R2", "TGFbR1_R2", "TGFbR1_R2", "ACVR1B_TGF…
#> $ agonist <chr> "TGFb agonist", "TGFb agonist", "TGFb agonist", "T…
#> $ antagonist <chr> "TGFb antagonist", "TGFb antagonist", "TGFb antago…
#> $ co_A_receptor <chr> "", "", "", "", "", "", "", "", "", "", "", "", ""…
#> $ co_I_receptor <chr> "TGFb inhibition receptor", "TGFb inhibition recep…
#> $ evidence <chr> "KEGG: hsa04350", "KEGG: hsa04350", "KEGG: hsa0435…
#> $ annotation <chr> "Secreted Signaling", "Secreted Signaling", "Secre…
#> $ interaction_name_2 <chr> "TGFB1 - (TGFBR1+TGFBR2)", "TGFB2 - (TGFBR1+TGFBR2…
# use a subset of CellChatDB for cell-cell communication analysis
#CellChatDB.use <- subsetDB(CellChatDB, search = "Secreted Signaling") # use Secreted Signaling
# use all CellChatDB for cell-cell communication analysis
CellChatDB.use <- CellChatDB # simply use the default CellChatDB
# set the used database in the object
cellchat@DB <- CellChatDB.usecellchat <- subsetData(cellchat) # subset the expression data of signaling genes for saving computation cost
future::plan("multiprocess", workers = 4) # do parallelStrategy 'multiprocess' is deprecated in future (>= 1.20.0). Instead, explicitly specify either 'multisession' or 'multicore'. In the current R session, 'multiprocess' equals 'multisession'.[ONE-TIME WARNING] Forked processing ('multicore') is not supported when running R from RStudio because it is considered unstable. For more details, how to control forked processing or not, and how to silence this warning in future R sessions, see ?parallelly::supportsMulticorecellchat <- identifyOverExpressedGenes(cellchat)
cellchat <- identifyOverExpressedInteractions(cellchat)
cellchat <- projectData(cellchat, PPI.human)#note trim is referring to Cellchats method for calculating the truncated mean, default is 0.25 meaning expression will be set to 0 if 25% or less of cells of a given cell type express the ligand/receptor. By default, I've changed this to 10% to account for genes expressed in a small portion of cells, that could still be biologically relevant. I beleive this is more similar to CellphoneDB cut offs anyway.
cellchat <- computeCommunProb(cellchat, raw.use = TRUE, type = "truncatedMean", trim = 0.1)
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|=====================================================================================================================================================| 100%# Filter out the cell-cell communication if there are only few number of cells in certain cell groups
cellchat <- filterCommunication(cellchat, min.cells = 10)The cell-cell communication related with the following cell groups are excluded due to the few number of cells: EC_cycling Eosinophil/basophil Erythrocytes Glial_1 Immune_recruiting_pericyte Neuroblast Oesophagus_fibroblast Export results as a data frame
df.net <- subsetCommunication(cellchat)
head(df.net)write.csv(df.net, "/nfs/team205/ao15/Megagut/Annotations_v3/disease_analysis/interactions/pooled_disease.remapped.allgenes.AP_SI.cellchat_output_230523.csv", row.names = TRUE)cellchat <- computeCommunProbPathway(cellchat)
cellchat <- aggregateNet(cellchat)groupSize <- as.numeric(table(cellchat@idents))
par(mfrow = c(1,2), xpd=TRUE)
netVisual_circle(cellchat@net$count, vertex.weight = groupSize, weight.scale = T, label.edge= F, title.name = "Number of interactions",vertex.label.cex = 0.5)
netVisual_circle(cellchat@net$weight, vertex.weight = groupSize, weight.scale = T, label.edge= F, title.name = "Interaction weights/strength", vertex.label.cex = 0.5)
# Compute the network centrality scores
cellchat <- netAnalysis_computeCentrality(cellchat, slot.name = "netP") # the slot 'netP' means the inferred intercellular communication network of signaling pathwaysUNRELIABLE VALUE: One of the ‘future.apply’ iterations (‘future_sapply-1’) unexpectedly generated random numbers without declaring so. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'future.seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'future.seed = NULL', or set option 'future.rng.onMisuse' to "ignore".UNRELIABLE VALUE: One of the ‘future.apply’ iterations (‘future_sapply-2’) unexpectedly generated random numbers without declaring so. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'future.seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'future.seed = NULL', or set option 'future.rng.onMisuse' to "ignore".UNRELIABLE VALUE: One of the ‘future.apply’ iterations (‘future_sapply-3’) unexpectedly generated random numbers without declaring so. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'future.seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'future.seed = NULL', or set option 'future.rng.onMisuse' to "ignore".UNRELIABLE VALUE: One of the ‘future.apply’ iterations (‘future_sapply-4’) unexpectedly generated random numbers without declaring so. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'future.seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'future.seed = NULL', or set option 'future.rng.onMisuse' to "ignore".# Visualize the computed centrality scores using heatmap, allowing ready identification of major signaling roles of cell groups
#netAnalysis_signalingRole_network(cellchat, signaling = pathways.show, width = 8, height = 2.5, font.size = 10)# Signaling role analysis on the aggregated cell-cell communication network from all signaling pathways
ht1 <- netAnalysis_signalingRole_heatmap(cellchat, pattern = "outgoing", width = 5, height = 10, font.size = 3)
ht2 <- netAnalysis_signalingRole_heatmap(cellchat, pattern = "incoming", width = 5, height = 10, font.size = 3)
pdf(file ="all_genes_plots/diseaseonly/cellchat_signalling_heatmap.pdf", width = 10, height =20)
ht1 + ht2Heatmap/annotation names are duplicated: Relative strengthdev.off()null device
1 #personally I find this plot one of the most useful to look at an overview of signaling pathways, and then use the other functions to plot them in whichever way makes sense#save cellchat object
saveRDS(cellchat, file = "/nfs/team205/ao15/Megagut/Annotations_v3/disease_analysis/interactions/pooled_disease.remapped.allgenes.AP_SI.cellchat_object.rds")# Signaling role analysis on the aggregated cell-cell communication network from all signaling pathways
gg1 <- netAnalysis_signalingRole_scatter(cellchat)Signaling role analysis on the aggregated cell-cell communication network from all signaling pathways
`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.levels(cellchat@idents) [1] "B_GC_I" "B_GC_II"
[3] "B_memory" "B_naive"
[5] "B_plasma_IgA1" "B_plasma_IgA2"
[7] "B_plasma_IgG" "B_plasma_IgM"
[9] "B_plasmablast" "BEST4_enterocyte_colonocyte"
[11] "Crypt_fibroblast_PI16" "DC_cDC1"
[13] "DC_cDC2" "DC_langerhans"
[15] "DC_migratory" "DC_pDC"
[17] "EC_arterial_1" "EC_arterial_2"
[19] "EC_capillary" "EC_cycling"
[21] "EC_lymphatic" "EC_venous"
[23] "Enterocyte" "Enteroendocrine"
[25] "Eosinophil/basophil" "Epithelial_stem"
[27] "Erythrocytes" "Fibroblast_reticular"
[29] "Follicular_DC" "gdT"
[31] "gdT_naive" "Glial_1"
[33] "Glial_2" "Glial/Enteric_neural_crest"
[35] "Goblet" "Goblet_cycling"
[37] "Goblet_progenitor" "ILC3"
[39] "Immune_recruiting_pericyte" "Lamina_propria_fibroblast_ADAMDEC1"
[41] "Macrophage" "Macrophage_LYVE1"
[43] "Macrophage_MMP9" "Macrophage_TREM2"
[45] "MAIT" "Mast"
[47] "Microfold" "Monocyte"
[49] "Mucous_gland_neck" "Myofibroblast"
[51] "Neuroblast" "NK_CD16"
[53] "NK_CD56bright" "Oesophagus_fibroblast"
[55] "Oral_mucosa_fibroblast" "Paneth"
[57] "Pericyte" "Rectum_fibroblast"
[59] "Surface_foveolar" "T/NK_cycling"
[61] "TA" "Tfh"
[63] "Tfh_naive" "Tnaive/cm_CD4"
[65] "Tnaive/cm_CD8" "Treg"
[67] "Treg_IL10" "Trm_CD4"
[69] "Trm_CD8" "Trm_Th17"
[71] "Trm/em_CD8" "Tuft"
[73] "Vascular_smooth_muscle" "Villus_fibroblast_F3" gg1
#this plot is helpful to look at the overall signaling roles of cell types (ie. are they mostly sending signals, receiving signals or a mixture of both)mat <- cellchat@net$weight
par(mfrow = c(2,2), xpd=TRUE)
for (i in 1:nrow(mat)) {
mat2 <- matrix(0, nrow = nrow(mat), ncol = ncol(mat), dimnames = dimnames(mat))
mat2[i, ] <- mat[i, ]
netVisual_circle(mat2, vertex.weight = groupSize, weight.scale = T, edge.weight.max = max(mat), title.name = rownames(mat)[i])
}
number of items to replace is not a multiple of replacement length
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data length exceeds size of matrix
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cellchat@netP$pathways [1] "COLLAGEN" "MHC-I" "MIF" "APP" "MHC-II" "CLEC" "LAMININ" "MK"
[9] "CD99" "CXCL" "GALECTIN" "FN1" "CCL" "CD45" "SPP1" "VISFATIN"
[17] "JAM" "ITGB2" "THBS" "PARs" "CD22" "ADGRE5" "CEACAM" "ICAM"
[25] "PECAM1" "ANGPTL" "PTN" "BAFF" "NOTCH" "TENASCIN" "CDH" "CDH1"
[33] "COMPLEMENT" "GAS" "SELL" "VCAM" "VEGF" "GUCA" "LCK" "GRN"
[41] "MPZ" "NECTIN" "EGF" "DESMOSOME" "SELE" "CD46" "CHEMERIN" "EPHB"
[49] "THY1" "PDGF" "ESAM" "TIGIT" "CD40" "IL16" "ANNEXIN" "SEMA3"
[57] "EDN" "SELPLG" "HSPG" "ncWNT" "FGF" "CDH5" "SEMA4" "GDF"
[65] "EPHA" "NRG" "NCAM" "CALCR" "TNF" "TGFb" "PROS" "ANGPT"
[73] "IGF" "IFN-II" "CD23" "CD86" "CSF" "TWEAK" "APRIL" "BTLA"
[81] "PERIOSTIN" "CADM" "CD96" "IL1" "CD39" "SEMA6" "BAG" "L1CAM"
[89] "CD34" "ALCAM" "CD6" "IL2" "CD70" "PTPRM" "RELN" "SEMA7"
[97] "CD226" "SN" "ANXA1" "BMP" "OCLN" "WNT" "LT" "XCR"
[105] "OSM" "FLT3" "KIT" "PVR" "TRAIL" "IL6" "LIGHT" "MADCAM"
[113] "RANKL" "SEMA5" "AGRN" "CD80" "ACTIVIN" "GP1BA" "ICOS" "LIFR"
[121] "CSF3" "CSPG4" "CHAD" "GCG" "NRXN" "CX3C" "NPY" "VEGI"
[129] "NEGR" "PD-L1" "HGF" "NT" "IL10" "FASLG" "SAA" "IL17"
[137] "PDL2" "OX40" "GIPR" "CD137" "CLDN" "AGT" "NGF" pathways.show <- c("MHC-II")
# Hierarchy plot
# Here we define `vertex.receive` so that the left portion of the hierarchy plot shows signaling to fibroblast and the right portion shows signaling to immune cells
vertex.receiver = seq(4,8) # a numeric vector.
netVisual_aggregate(cellchat, signaling = pathways.show, vertex.receiver = vertex.receiver)number of items to replace is not a multiple of replacement length
pathways.show <- c("CCL")
# Heatmap
par(mfrow=c(1,1))
pdf(file ="all_genes_plots/diseaseonly/cellchat_CCLheatmap.pdf", width = 20, height =16)
netVisual_heatmap(cellchat, signaling = pathways.show, color.heatmap = "Reds",width=50,height=50,font.size=6)Do heatmap based on a single object pathways.show <- c("CXCL")
# Heatmap
par(mfrow=c(1,1))
pdf(file ="all_genes_plots/diseaseonly/cellchat_CXCLheatmap.pdf", width = 20, height =16)
netVisual_heatmap(cellchat, signaling = pathways.show, color.heatmap = "Reds",width=50,height=50,font.size=6)Do heatmap based on a single object pathways.show <- c("MHC-II")
# Heatmap
par(mfrow=c(1,1))
pdf(file ="all_genes_plots/diseaseonly/cellchat_MHCIIheatmap.pdf", width = 20, height =16)
netVisual_heatmap(cellchat, signaling = pathways.show, color.heatmap = "Reds",width=50,height=50,font.size=4)Do heatmap based on a single object par(mfrow=c(1,1))
pdf(file ="all_genes_plots/diseaseonly/cellchat_CCLchord.pdf", width = 20, height =16)
netVisual_aggregate(cellchat, signaling = 'CCL', layout = "chord", remove.isolate = TRUE, scale=TRUE)
dev.off()png
2 par(mfrow=c(1,1))
pdf(file ="all_genes_plots/diseaseonly/cellchat_CXCLchord.pdf", width = 20, height =16)
netVisual_aggregate(cellchat, signaling = 'CXCL', layout = "chord", remove.isolate = TRUE, scale=TRUE)
dev.off()png
2 par(mfrow=c(1,1))
pdf(file ="all_genes_plots/diseaseonly/cellchat_MHCIIchord.pdf", width = 20, height =16)
netVisual_aggregate(cellchat, signaling = 'MHC-II', layout = "chord", remove.isolate = TRUE, scale=TRUE)
dev.off()png
2 # Chord diagram
#group.cellType <- c(rep("Fibro", 2), rep("B", 3), rep("SMG", 3)) # grouping cell clusters into fibroblast, DC and TC cells
#names(group.cellType) <- levels(cellchat@idents)
#netVisual_chord_cell(cellchat, signaling = pathways.show, group = group.cellType, title.name = paste0(pathways.show, " signaling network"))
netVisual_chord_cell(cellchat, signaling = pathways.show, title.name = paste0(pathways.show, " signaling network"))Plot the aggregated cell-cell communication network at the signaling pathway level
netAnalysis_contribution(cellchat, signaling = "CXCL")
#to get a cursory look at which L-R pairs within a given network, can also look at the full overview on the cellchat database http://www.cellchat.org/cellchatdb/netAnalysis_contribution(cellchat, signaling = "CCL")
#to get a cursory look at which L-R pairs within a given network, can also look at the full overview on the cellchat database http://www.cellchat.org/cellchatdb/netAnalysis_contribution(cellchat, signaling = "MHC-II")
#to get a cursory look at which L-R pairs within a given network, can also look at the full overview on the cellchat database http://www.cellchat.org/cellchatdb/netAnalysis_contribution(cellchat, signaling = "THBS")
#to get a cursory look at which L-R pairs within a given network, can also look at the full overview on the cellchat database http://www.cellchat.org/cellchatdb/pathways.show = "CCL"
pairLR.CCL <- extractEnrichedLR(cellchat, signaling = pathways.show, geneLR.return = FALSE)
LR.show <- pairLR.CCL[6,]
pdf(file ="all_genes_plots/diseaseonly/cellchat_CCLnetvis_individual.pdf", width = 20, height =16)
netVisual_individual(cellchat, signaling = pathways.show, pairLR.use = LR.show, layout = "chord")[[1]]netVisual_aggregate(cellchat, signaling = 'CXCL', layout = "circle")
netVisual_aggregate(cellchat, signaling = 'SAA', layout = "circle")
netVisual_bubble(cellchat, sources.use = 57, remove.isolate = FALSE)Comparing communications on a single object 
pdf(file ="all_genes_plots/diseaseonly/cellchat_MUC6source_netvisualbubble.pdf", width = 20, height =30)
netVisual_bubble(cellchat, sources.use = 57, remove.isolate = FALSE)Comparing communications on a single object pdf(file ="all_genes_plots/diseaseonly/cellchat_MUC6target_netvisualbubble.pdf", width = 20, height =30)
netVisual_bubble(cellchat, targets.use = 57, remove.isolate = FALSE)Comparing communications on a single object pdf(file ="all_genes_plots/diseaseonly/cellchat_OralFibrosource_netvisualbubble.pdf", width = 20, height =40)
netVisual_bubble(cellchat, sources.use = 63, remove.isolate = FALSE)Comparing communications on a single object haven’t run any of this yet –>
netVisual_chord_gene <- function(object, slot.name = "net", color.use = NULL,
signaling = NULL, pairLR.use = NULL, net = NULL,
sources.use = NULL, targets.use = NULL,
lab.cex = 0.8,small.gap = 1, big.gap = 10, annotationTrackHeight = c(0.03),
link.visible = TRUE, scale = FALSE, directional = 1, link.target.prop = TRUE, reduce = -1,
transparency = 0.4, link.border = NA,
title.name = NULL, legend.pos.x = 20, legend.pos.y = 20, show.legend = TRUE,
thresh = 0.05,
...){
if (!is.null(pairLR.use)) {
if (!is.data.frame(pairLR.use)) {
stop("pairLR.use should be a data frame with a signle column named either 'interaction_name' or 'pathway_name' ")
} else if ("pathway_name" %in% colnames(pairLR.use)) {
message("slot.name is set to be 'netP' when pairLR.use contains signaling pathways")
slot.name = "netP"
}
}
if (!is.null(pairLR.use) & !is.null(signaling)) {
stop("Please do not assign values to 'signaling' when using 'pairLR.use'")
}
if (is.null(net)) {
prob <- slot(object, "net")$prob
pval <- slot(object, "net")$pval
prob[pval > thresh] <- 0
net <- reshape2::melt(prob, value.name = "prob")
colnames(net)[1:3] <- c("source","target","interaction_name")
pairLR = dplyr::select(object@LR$LRsig, c("interaction_name_2", "pathway_name", "ligand", "receptor" ,"annotation","evidence"))
idx <- match(net$interaction_name, rownames(pairLR))
temp <- pairLR[idx,]
net <- cbind(net, temp)
}
if (!is.null(signaling)) {
pairLR.use <- data.frame()
for (i in 1:length(signaling)) {
pairLR.use.i <- searchPair(signaling = signaling[i], pairLR.use = object@LR$LRsig, key = "pathway_name", matching.exact = T, pair.only = T)
pairLR.use <- rbind(pairLR.use, pairLR.use.i)
}
}
if (!is.null(pairLR.use)){
if ("interaction_name" %in% colnames(pairLR.use)) {
net <- subset(net,interaction_name %in% pairLR.use$interaction_name)
} else if ("pathway_name" %in% colnames(pairLR.use)) {
net <- subset(net, pathway_name %in% as.character(pairLR.use$pathway_name))
}
}
if (slot.name == "netP") {
net <- dplyr::select(net, c("source","target","pathway_name","prob"))
net$source_target <- paste(net$source, net$target, sep = "sourceTotarget")
net <- net %>% dplyr::group_by(source_target, pathway_name) %>% dplyr::summarize(prob = sum(prob))
a <- stringr::str_split(net$source_target, "sourceTotarget", simplify = T)
net$source <- as.character(a[, 1])
net$target <- as.character(a[, 2])
net$ligand <- net$pathway_name
net$receptor <- " "
}
# keep the interactions associated with sources and targets of interest
if (!is.null(sources.use)){
if (is.numeric(sources.use)) {
sources.use <- levels(object@idents)[sources.use]
}
net <- subset(net, source %in% sources.use)
} else {
sources.use <- levels(object@idents)
}
if (!is.null(targets.use)){
if (is.numeric(targets.use)) {
targets.use <- levels(object@idents)[targets.use]
}
net <- subset(net, target %in% targets.use)
} else {
targets.use <- levels(object@idents)
}
# remove the interactions with zero values
df <- subset(net, prob > 0)
if (nrow(df) == 0) {
stop("No signaling links are inferred! ")
}
if (length(unique(net$ligand)) == 1) {
message("You may try the function `netVisual_chord_cell` for visualizing individual signaling pathway")
}
df$id <- 1:nrow(df)
# deal with duplicated sector names
ligand.uni <- unique(df$ligand)
for (i in 1:length(ligand.uni)) {
df.i <- df[df$ligand == ligand.uni[i], ]
source.uni <- unique(df.i$source)
for (j in 1:length(source.uni)) {
df.i.j <- df.i[df.i$source == source.uni[j], ]
df.i.j$ligand <- paste0(df.i.j$ligand, paste(rep(' ',j-1),collapse = ''))
df$ligand[df$id %in% df.i.j$id] <- df.i.j$ligand
}
}
receptor.uni <- unique(df$receptor)
for (i in 1:length(receptor.uni)) {
df.i <- df[df$receptor == receptor.uni[i], ]
target.uni <- unique(df.i$target)
for (j in 1:length(target.uni)) {
df.i.j <- df.i[df.i$target == target.uni[j], ]
df.i.j$receptor <- paste0(df.i.j$receptor, paste(rep(' ',j-1),collapse = ''))
df$receptor[df$id %in% df.i.j$id] <- df.i.j$receptor
}
}
cell.order.sources <- levels(object@idents)[levels(object@idents) %in% sources.use]
cell.order.targets <- levels(object@idents)[levels(object@idents) %in% targets.use]
df$source <- factor(df$source, levels = cell.order.sources)
df$target <- factor(df$target, levels = cell.order.targets)
# df.ordered.source <- df[with(df, order(source, target, -prob)), ]
# df.ordered.target <- df[with(df, order(target, source, -prob)), ]
df.ordered.source <- df[with(df, order(source, -prob)), ]
df.ordered.target <- df[with(df, order(target, -prob)), ]
order.source <- unique(df.ordered.source[ ,c('ligand','source')])
order.target <- unique(df.ordered.target[ ,c('receptor','target')])
# define sector order
order.sector <- c(order.source$ligand, order.target$receptor)
# define cell type color
if (is.null(color.use)){
color.use = scPalette(nlevels(object@idents))
names(color.use) <- levels(object@idents)
color.use <- color.use[levels(object@idents) %in% as.character(union(df$source,df$target))]
} else if (is.null(names(color.use))) {
names(color.use) <- levels(object@idents)
color.use <- color.use[levels(object@idents) %in% as.character(union(df$source,df$target))]
}
# define edge color
edge.color <- color.use[as.character(df.ordered.source$source)]
names(edge.color) <- as.character(df.ordered.source$source)
# define grid colors
grid.col.ligand <- color.use[as.character(order.source$source)]
names(grid.col.ligand) <- as.character(order.source$source)
grid.col.receptor <- color.use[as.character(order.target$target)]
names(grid.col.receptor) <- as.character(order.target$target)
grid.col <- c(as.character(grid.col.ligand), as.character(grid.col.receptor))
names(grid.col) <- order.sector
df.plot <- df.ordered.source[ ,c('ligand','receptor','prob')]
if (directional == 2) {
link.arr.type = "triangle"
} else {
link.arr.type = "big.arrow"
}
circos.clear()
chordDiagram(df.plot,
order = order.sector,
col = edge.color,
grid.col = grid.col,
transparency = transparency,
link.border = link.border,
directional = directional,
direction.type = c("diffHeight","arrows"),
link.arr.type = link.arr.type,
annotationTrack = "grid",
annotationTrackHeight = annotationTrackHeight,
preAllocateTracks = list(track.height = max(strwidth(order.sector))),
small.gap = small.gap,
big.gap = big.gap,
link.visible = link.visible,
scale = scale,
link.target.prop = link.target.prop,
reduce = reduce,
...)
circos.track(track.index = 1, panel.fun = function(x, y) {
xlim = get.cell.meta.data("xlim")
xplot = get.cell.meta.data("xplot")
ylim = get.cell.meta.data("ylim")
sector.name = get.cell.meta.data("sector.index")
circos.text(mean(xlim), ylim[1], sector.name, facing = "bending", niceFacing = TRUE, adj=c(0.5,0.01),cex = lab.cex)
}, bg.border = NA)
# https://jokergoo.github.io/circlize_book/book/legends.html
if (show.legend) {
lgd <- ComplexHeatmap::Legend(at = names(color.use), type = "grid", legend_gp = grid::gpar(fill = color.use), title = "Cell State")
ComplexHeatmap::draw(lgd, x = unit(1, "npc")-unit(legend.pos.x, "mm"), y = unit(legend.pos.y, "mm"), just = c("right", "bottom"))
}
circos.clear()
if(!is.null(title.name)){
text(-0, 1.02, title.name, cex=1)
}
gg <- recordPlot()
return(gg)
}netVisual_chord_gene(cellchat, signaling = c("SAA"),legend.pos.x = 8, scale=FALSE)#, sources.use = c(1,2,3,4,5,6,7), targets.use = c(1,2,3,4,5,6,7), lab.cex=1, color.use = c('#CD6600', '#FF8C00', '#CD00CD','#0000EE', '#8FBC8F','#C1FFC1','#228B22')) colours = c('#db9602',# 'B_GC_I':
'#e2d138',# 'B_GC_I':
'#a33c22',# 'B_memory':
'#9b0319',# 'B_naive':
'#f76c56',# 'B_plasma_IgA1':
'#d6558d',# 'B_plasma_IgA2':
'#632f17',# 'B_plasma_IgG':
'#c66d31',# 'B_plasma_IgM':
'#9e53db',# 'B_plasmablast':
'#8a4682',# 'B_preB':
'#d34794',# 'B_proB':
'#39997c',# 'BEST4_enterocyte_colonocyte':
'#bd7879',# 'Crypt_fibroblast_PI16':
'#8c543f',# 'DC_cDC1':
'#cfdb65',# 'DC_cDC2':
'#c7a642',# 'DC_langerhans':
'#e6a519',# 'DC_migratory':
'#bdb197',# 'DC_pDC':
'#fa6e6e',# 'EC_arterial_1':
'#ca6092',# 'EC_arterial_2':
'#855f9a',# 'EC_capillary':
'#fac06e',# 'EC_cycling':
'#999999',# 'EC_lymphatic':
'#2a4858',# 'EC_venous':
'#DBA507',# 'Enteric_neural_crest_cycling':
'#e1b5e6',# 'Enterocyte':
'#68b7fc',# 'Enteroendocrine':
'#8b4eba',# 'Eosinophil/basophil':
'#c924b9',# 'Epithelial_stem':
'#0e539c',# 'Erythrocytes':
'#f0c134',# 'Fibroblast_reticular':
'#f0982c',# 'Follicular_DC':
'#3fafb5',# 'gdT':
'#26daf2',# 'gdT_naive':
'#8EC7D2',# 'Glial_1':
'#0D6986',# 'Glial_2':
'#053240',# 'Glial_3':
'#a8c545',# 'Glial/Enteric_neural_crest':
'#6c9939',# 'Goblet':
'#d1d14f',# 'Goblet_cycling':
'#e9f7ad',# 'Goblet_progenitor':
'#778c00',# 'ILC3':
'#AAC789',# 'Immune_recruiting_pericyte':
'#e95e50',# 'Lamina_propria_fibroblast_ADAMDEC1':
'#486626',# 'Macrophage':
'#caf9cf',# 'Macrophage_CD5L':
'#8fd9d0',# 'Macrophage_LYVE1':
'#a5f002',# 'Macrophage_MMP9':
'#42c7ac',# 'Macrophage_TREM2':
'#21b796',# 'MAIT':
'#826e91',# 'Mast':
'#c730aa',# 'Megakaryocyte/platelet':
'#8F6592',# 'Mesothelium':
'#e55b85',# 'Microfold':
'#2a497a',# 'Mono/neutrophil_MPO':
'#5baf07',# 'Monocyte':
'#f7b37c',# 'Mucous_gland_neck':
'#CCAE91',# 'Myofibroblast':
'#c50637',# 'Neuroblast':
'#0c1e0e',# 'NK_CD16':
'#3f8c08',# 'NK_CD56bright':
'#63A0C0',# 'Oesophagus_fibroblast':
'#303267',# 'Oral_mucosa_fibroblast':
'#79508f',# 'Paneth':
'#437356',# 'Pericyte':
'#522e25',# 'Rectum_fibroblast':
'#d9b74a',# 'Surface_foveolar':
'#c260ff',# 'T/NK_cycling':
'#b85f1c',# 'TA':
'#5e0b30',# 'Tfh':
'#5e3c55',# 'Tfh_naive':
'#9c53bc',# 'Tnaive/cm_CD4':
'#5ca4ce',# 'Tnaive/cm_CD8':
'#f98261',# 'Treg':
'#e5c510',# 'Treg_IL10':
'#8107ed',# 'Trm_CD4':
'#2844c1',# 'Trm_CD8':
'#1e093f',# 'Trm_Th17':
'#256b87',# 'Trm/em_CD8':
'#9d9dff',# 'Tuft':
'#1E4147',# 'Vascular_smooth_muscle':
'#d64582'# 'Villus_fibroblast_F3':
)netVisual_chord_gene(cellchat, signaling = c("SAA"), legend.pos.x = 8, scale=FALSE,sources.use = c(30,32,56,57,63), targets.use = c(30,32,56,57,63), lab.cex=1,color.use = colours) netVisual_chord_gene <- function(object, slot.name = "net", color.use = NULL,
signaling = NULL, pairLR.use = NULL, net = NULL,
sources.use = NULL, targets.use = NULL,
lab.cex = 0.8,small.gap = 1, big.gap = 10, annotationTrackHeight = c(0.03),
link.visible = TRUE, scale = FALSE, directional = 1, link.target.prop = TRUE, reduce = -1,
transparency = 0.4, link.border = NA,
title.name = NULL, legend.pos.x = 20, legend.pos.y = 20, show.legend = TRUE,
thresh = 0.05,
...){
if (!is.null(pairLR.use)) {
if (!is.data.frame(pairLR.use)) {
stop("pairLR.use should be a data frame with a signle column named either 'interaction_name' or 'pathway_name' ")
} else if ("pathway_name" %in% colnames(pairLR.use)) {
message("slot.name is set to be 'netP' when pairLR.use contains signaling pathways")
slot.name = "netP"
}
}
if (!is.null(pairLR.use) & !is.null(signaling)) {
stop("Please do not assign values to 'signaling' when using 'pairLR.use'")
}
if (is.null(net)) {
prob <- slot(object, "net")$prob
pval <- slot(object, "net")$pval
prob[pval > thresh] <- 0
net <- reshape2::melt(prob, value.name = "prob")
colnames(net)[1:3] <- c("source","target","interaction_name")
pairLR = dplyr::select(object@LR$LRsig, c("interaction_name_2", "pathway_name", "ligand", "receptor" ,"annotation","evidence"))
idx <- match(net$interaction_name, rownames(pairLR))
temp <- pairLR[idx,]
net <- cbind(net, temp)
}
if (!is.null(signaling)) {
pairLR.use <- data.frame()
for (i in 1:length(signaling)) {
pairLR.use.i <- searchPair(signaling = signaling[i], pairLR.use = object@LR$LRsig, key = "pathway_name", matching.exact = T, pair.only = T)
pairLR.use <- rbind(pairLR.use, pairLR.use.i)
}
}
if (!is.null(pairLR.use)){
if ("interaction_name" %in% colnames(pairLR.use)) {
net <- subset(net,interaction_name %in% pairLR.use$interaction_name)
} else if ("pathway_name" %in% colnames(pairLR.use)) {
net <- subset(net, pathway_name %in% as.character(pairLR.use$pathway_name))
}
}
if (slot.name == "netP") {
net <- dplyr::select(net, c("source","target","pathway_name","prob"))
net$source_target <- paste(net$source, net$target, sep = "sourceTotarget")
net <- net %>% dplyr::group_by(source_target, pathway_name) %>% dplyr::summarize(prob = sum(prob))
a <- stringr::str_split(net$source_target, "sourceTotarget", simplify = T)
net$source <- as.character(a[, 1])
net$target <- as.character(a[, 2])
net$ligand <- net$pathway_name
net$receptor <- " "
}
# keep the interactions associated with sources and targets of interest
if (!is.null(sources.use)){
if (is.numeric(sources.use)) {
sources.use <- levels(object@idents)[sources.use]
}
net <- subset(net, source %in% sources.use)
} else {
sources.use <- levels(object@idents)
}
if (!is.null(targets.use)){
if (is.numeric(targets.use)) {
targets.use <- levels(object@idents)[targets.use]
}
net <- subset(net, target %in% targets.use)
} else {
targets.use <- levels(object@idents)
}
# remove the interactions with zero values
df <- subset(net, prob > 0)
if (nrow(df) == 0) {
stop("No signaling links are inferred! ")
}
if (length(unique(net$ligand)) == 1) {
message("You may try the function `netVisual_chord_cell` for visualizing individual signaling pathway")
}
df$id <- 1:nrow(df)
# deal with duplicated sector names
ligand.uni <- unique(df$ligand)
for (i in 1:length(ligand.uni)) {
df.i <- df[df$ligand == ligand.uni[i], ]
source.uni <- unique(df.i$source)
for (j in 1:length(source.uni)) {
df.i.j <- df.i[df.i$source == source.uni[j], ]
df.i.j$ligand <- paste0(df.i.j$ligand, paste(rep(' ',j-1),collapse = ''))
df$ligand[df$id %in% df.i.j$id] <- df.i.j$ligand
}
}
receptor.uni <- unique(df$receptor)
for (i in 1:length(receptor.uni)) {
df.i <- df[df$receptor == receptor.uni[i], ]
target.uni <- unique(df.i$target)
for (j in 1:length(target.uni)) {
df.i.j <- df.i[df.i$target == target.uni[j], ]
df.i.j$receptor <- paste0(df.i.j$receptor, paste(rep(' ',j-1),collapse = ''))
df$receptor[df$id %in% df.i.j$id] <- df.i.j$receptor
}
}
cell.order.sources <- levels(object@idents)[levels(object@idents) %in% sources.use]
cell.order.targets <- levels(object@idents)[levels(object@idents) %in% targets.use]
df$source <- factor(df$source, levels = cell.order.sources)
df$target <- factor(df$target, levels = cell.order.targets)
# df.ordered.source <- df[with(df, order(source, target, -prob)), ]
# df.ordered.target <- df[with(df, order(target, source, -prob)), ]
df.ordered.source <- df[with(df, order(source, -prob)), ]
df.ordered.target <- df[with(df, order(target, -prob)), ]
order.source <- unique(df.ordered.source[ ,c('ligand','source')])
order.target <- unique(df.ordered.target[ ,c('receptor','target')])
# define sector order
order.sector <- c(order.source$ligand, order.target$receptor)
# define cell type color
if (is.null(color.use)){
color.use = scPalette(nlevels(object@idents))
names(color.use) <- levels(object@idents)
color.use <- color.use[levels(object@idents) %in% as.character(union(df$source,df$target))]
} else if (is.null(names(color.use))) {
names(color.use) <- levels(object@idents)
color.use <- color.use[levels(object@idents) %in% as.character(union(df$source,df$target))]
}
# define edge color
edge.color <- color.use[as.character(df.ordered.source$source)]
names(edge.color) <- as.character(df.ordered.source$source)
# define grid colors
grid.col.ligand <- color.use[as.character(order.source$source)]
names(grid.col.ligand) <- as.character(order.source$source)
grid.col.receptor <- color.use[as.character(order.target$target)]
names(grid.col.receptor) <- as.character(order.target$target)
grid.col <- c(as.character(grid.col.ligand), as.character(grid.col.receptor))
names(grid.col) <- order.sector
df.plot <- df.ordered.source[ ,c('ligand','receptor','prob')]
if (directional == 2) {
link.arr.type = "triangle"
} else {
link.arr.type = "big.arrow"
}
circos.clear()
chordDiagram(df.plot,
order = order.sector,
col = edge.color,
grid.col = grid.col,
transparency = transparency,
link.border = link.border,
directional = directional,
direction.type = c("diffHeight","arrows"),
link.arr.type = link.arr.type,
annotationTrack = "grid",
annotationTrackHeight = annotationTrackHeight,
preAllocateTracks = list(track.height = max(strwidth(order.sector))),
small.gap = small.gap,
big.gap = big.gap,
link.visible = link.visible,
scale = scale,
link.target.prop = link.target.prop,
reduce = reduce,
...)
circos.track(track.index = 1, panel.fun = function(x, y) {
xlim = get.cell.meta.data("xlim")
xplot = get.cell.meta.data("xplot")
ylim = get.cell.meta.data("ylim")
sector.name = get.cell.meta.data("sector.index")
circos.text(mean(xlim), ylim[1], sector.name,facing = "clockwise", niceFacing = TRUE, adj=c(0.5,0.01),cex = lab.cex)
}, bg.border = NA)
# https://jokergoo.github.io/circlize_book/book/legends.html
if (show.legend) {
lgd <- ComplexHeatmap::Legend(at = names(color.use), type = "grid", legend_gp = grid::gpar(fill = color.use), title = "Cell State")
ComplexHeatmap::draw(lgd, x = unit(1, "npc")-unit(legend.pos.x, "mm"), y = unit(legend.pos.y, "mm"), just = c("right", "bottom"))
}
circos.clear()
if(!is.null(title.name)){
text(-0, 1.02, title.name, cex=1)
}
gg <- recordPlot()
return(gg)
}netVisual_chord_gene(cellchat, signaling = c("CXCL"), legend.pos.x = 8, scale=FALSE,sources.use = c(57,63), lab.cex=0.5,color.use = colours) netVisual_chord_gene(cellchat, signaling = c("CXCL"), legend.pos.x = 8, scale=TRUE,sources.use = c(57,63),targets.use =c(24,29,46,50,60,62,69,70,72,73), lab.cex=0.5,color.use = colours) pdf(file ="all_genes_plots/cellchat_MUC6OralFibrosource_cxclchord.pdf", width = 15, height =15)
netVisual_chord_gene(cellchat, signaling = c("CXCL"), legend.pos.x = 8, scale=TRUE,sources.use = c(57,63),targets.use =c(24,29,46,50,60,62,69,70,72,73), lab.cex=0.5,color.use = colours) netVisual_chord_gene(cellchat, signaling = c("CXCL"), legend.pos.x = 8, scale=TRUE,sources.use = c(57), lab.cex=1,color.use = colours) pdf(file ="all_genes_plots/cellchat_MUC6source_cxclchord.pdf", width = 15, height =15)
netVisual_chord_gene(cellchat, signaling = c("CXCL"), legend.pos.x = 8, scale=TRUE,sources.use = c(57), lab.cex=1,color.use = colours) netVisual_chord_gene(cellchat, signaling = c("CCL"), legend.pos.x = 8, scale=FALSE,sources.use = c(57,63), lab.cex=0.5,color.use = colours) netVisual_chord_gene(cellchat, signaling = c("CXCL"), legend.pos.x = 8, scale=FALSE,sources.use = c(57,63), lab.cex=0.5,color.use = colours) netVisual_chord_gene(cellchat, signaling = c("MHC-II"), legend.pos.x = 8, scale=FALSE,sources.use = c(57), lab.cex=0.5,color.use = colours) code from previous notebook
netVisual_chord_gene(cellchat, signaling = c("IL6"),legend.pos.x = 8, scale=FALSE, sources.use = c(1,2,3,4,5,6,7), targets.use = c(1,2,3,4,5,6,7), lab.cex=1, color.use = c('#CD6600', '#FF8C00', '#CD00CD','#0000EE', '#8FBC8F','#C1FFC1','#228B22')) netVisual_chord_gene(cellchat, signaling = c("CCL"),legend.pos.x = 8, scale=TRUE, sources.use = c(5,6,7), targets.use = c(1,2,3,4,5,6,7), lab.cex=0.5, color.use = c('#CD6600', '#FF8C00', '#CD00CD', '#0000EE', '#8FBC8F','#C1FFC1','#228B22'))
netVisual_chord_gene(cellchat, signaling = c("IL6"),legend.pos.x = 8, scale=TRUE, sources.use = c(5,6,7), targets.use = c(1,2,3,4), lab.cex=1.5, color.use = c('#CD6600', '#FF8C00', '#CD00CD', '#4169E1', '#0000EE', '#ADD8E6', '#8FBC8F','#C1FFC1','#228B22','#6495ED'))
netVisual_chord_gene(cellchat, signaling = c("IL6"),legend.pos.x = 8, scale=TRUE, lab.cex=1, color.use = c('#CD6600', '#FF8C00', '#CD00CD','#0000EE', '#8FBC8F','#C1FFC1','#228B22'))Below is just changes to the default circle/chord plots from the source code, the default option is to have the labels sticking out from the plot instead of wrapping around this can be changed in circos.text setting facing (https://www.rdocumentation.org/packages/circlize/versions/0.4.13/topics/circos.text)
#HLA-DRA
pathways.show <- c("MHC-II")
pairLR.MHCII <- extractEnrichedLR(cellchat, signaling = pathways.show, geneLR.return = FALSE)
LR.show <- pairLR.MHCII[11,] #HLA-DRA is 10 and HLA-DRB1 is 11
netVisual_individual(cellchat, signaling = pathways.show, pairLR.use = LR.show, layout = "chord", color.use = c('#CD6600', '#FF8C00', '#CD00CD', '#4169E1', '#0000EE', '#ADD8E6', '#8FBC8F','#C1FFC1','#228B22','#6495ED'),remove.isolate=TRUE)netVisual_individual <- function(object, signaling, signaling.name = NULL, pairLR.use = NULL, color.use = NULL, vertex.receiver = NULL, sources.use = NULL, targets.use = NULL, top = 1, remove.isolate = FALSE,
vertex.weight = 1, vertex.weight.max = NULL, vertex.size.max = NULL, vertex.label.cex = 0.8,
weight.scale = FALSE, edge.weight.max = NULL, edge.width.max=8, graphics.init = TRUE,
layout = c("circle","hierarchy","chord"), height = 5, thresh = 0.05, #from = NULL, to = NULL, bidirection = NULL,vertex.size = NULL,
group = NULL,cell.order = NULL,small.gap = 1, big.gap = 10, scale = FALSE, reduce = -1, show.legend = FALSE, legend.pos.x = 20, legend.pos.y = 20, nCol = NULL,
...) {
layout <- match.arg(layout)
# if (!is.null(vertex.size)) {
# warning("'vertex.size' is deprecated. Use `vertex.weight`")
# }
if (is.null(vertex.weight)) {
vertex.weight <- as.numeric(table(object@idents))
}
if (is.null(vertex.size.max)) {
if (length(unique(vertex.weight)) == 1) {
vertex.size.max <- 5
} else {
vertex.size.max <- 15
}
}
pairLR <- searchPair(signaling = signaling, pairLR.use = object@LR$LRsig, key = "pathway_name", matching.exact = T, pair.only = F)
if (is.null(signaling.name)) {
signaling.name <- signaling
}
net <- object@net
pairLR.use.name <- dimnames(net$prob)[[3]]
pairLR.name <- intersect(rownames(pairLR), pairLR.use.name)
if (!is.null(pairLR.use)) {
if (is.data.frame(pairLR.use)) {
pairLR.name <- intersect(pairLR.name, as.character(pairLR.use$interaction_name))
} else {
pairLR.name <- intersect(pairLR.name, as.character(pairLR.use))
}
if (length(pairLR.name) == 0) {
stop("There is no significant communication for the input L-R pairs!")
}
}
pairLR <- pairLR[pairLR.name, ]
prob <- net$prob
pval <- net$pval
prob[pval > thresh] <- 0
if (length(pairLR.name) > 1) {
pairLR.name.use <- pairLR.name[apply(prob[,,pairLR.name], 3, sum) != 0]
} else {
pairLR.name.use <- pairLR.name[sum(prob[,,pairLR.name]) != 0]
}
if (length(pairLR.name.use) == 0) {
stop(paste0('There is no significant communication of ', signaling.name))
} else {
pairLR <- pairLR[pairLR.name.use,]
}
nRow <- length(pairLR.name.use)
prob <- prob[,,pairLR.name.use]
pval <- pval[,,pairLR.name.use]
if (is.null(nCol)) {
nCol <- min(length(pairLR.name.use), 2)
}
if (length(dim(prob)) == 2) {
prob <- replicate(1, prob, simplify="array")
pval <- replicate(1, pval, simplify="array")
}
# prob <-(prob-min(prob))/(max(prob)-min(prob))
if (is.null(edge.weight.max)) {
edge.weight.max = max(prob)
}
if (layout == "hierarchy") {
if (graphics.init) {
par(mfrow=c(nRow,2), mar = c(5, 4, 4, 2) +0.1)
}
for (i in 1:length(pairLR.name.use)) {
signalName_i <- pairLR$interaction_name_2[i]
prob.i <- prob[,,i]
netVisual_hierarchy1(prob.i, vertex.receiver = vertex.receiver, sources.use = sources.use, targets.use = targets.use, remove.isolate = remove.isolate, top = top, color.use = color.use, vertex.weight = vertex.weight, vertex.weight.max = vertex.weight.max, vertex.size.max = vertex.size.max, weight.scale = weight.scale, edge.weight.max = edge.weight.max, edge.width.max=edge.width.max, title.name = signalName_i,...)
netVisual_hierarchy2(prob.i, vertex.receiver = setdiff(1:nrow(prob.i),vertex.receiver), sources.use = sources.use, targets.use = targets.use, remove.isolate = remove.isolate, top = top, color.use = color.use, vertex.weight = vertex.weight, vertex.weight.max = vertex.weight.max, vertex.size.max = vertex.size.max, weight.scale = weight.scale, edge.weight.max = edge.weight.max, edge.width.max=edge.width.max, title.name = signalName_i,...)
}
# grid.echo()
# gg <- grid.grab()
gg <- recordPlot()
} else if (layout == "circle") {
# par(mfrow=c(nRow,1))
if (graphics.init) {
par(mfrow = c(ceiling(length(pairLR.name.use)/nCol), nCol), xpd=TRUE)
}
gg <- vector("list", length(pairLR.name.use))
for (i in 1:length(pairLR.name.use)) {
signalName_i <- pairLR$interaction_name_2[i]
prob.i <- prob[,,i]
gg[[i]] <- netVisual_circle(prob.i, sources.use = sources.use, targets.use = targets.use, remove.isolate = remove.isolate, top = top, color.use = color.use, vertex.weight = vertex.weight, vertex.weight.max = vertex.weight.max, vertex.size.max = vertex.size.max, weight.scale = weight.scale, edge.weight.max = edge.weight.max, edge.width.max=edge.width.max, title.name = signalName_i,...)
}
} else if (layout == "chord") {
if (graphics.init) {
par(mfrow = c(ceiling(length(pairLR.name.use)/nCol), nCol), xpd=TRUE)
}
gg <- vector("list", length(pairLR.name.use))
for (i in 1:length(pairLR.name.use)) {
title.name <- pairLR$interaction_name_2[i]
net <- prob[,,i]
gg[[i]] <- netVisual_chord_cell_internal(net, color.use = color.use, sources.use = sources.use, targets.use = targets.use, remove.isolate = remove.isolate,
group = group, cell.order = cell.order,
lab.cex = vertex.label.cex,small.gap = small.gap, big.gap = big.gap,
scale = scale, reduce = reduce,
title.name = title.name, show.legend = show.legend, legend.pos.x = legend.pos.x, legend.pos.y = legend.pos.y)
}
}
return(gg)
}netVisual_chord_cell_internal <- function(net, color.use = NULL, group = NULL, cell.order = NULL,
sources.use = NULL, targets.use = NULL,
lab.cex = 0.8,small.gap = 1, big.gap = 10, annotationTrackHeight = c(0.03),
remove.isolate = FALSE, link.visible = TRUE, scale = FALSE, directional = 1, link.target.prop = TRUE, reduce = -1,
transparency = 0.4, link.border = NA,
title.name = NULL, show.legend = FALSE, legend.pos.x = 20, legend.pos.y = 20,...){
if (inherits(x = net, what = c("matrix", "Matrix"))) {
cell.levels <- union(rownames(net), colnames(net))
net <- reshape2::melt(net, value.name = "prob")
colnames(net)[1:2] <- c("source","target")
} else if (is.data.frame(net)) {
if (all(c("source","target", "prob") %in% colnames(net)) == FALSE) {
stop("The input data frame must contain three columns named as source, target, prob")
}
cell.levels <- as.character(union(net$source,net$target))
}
if (!is.null(cell.order)) {
cell.levels <- cell.order
}
net$source <- as.character(net$source)
net$target <- as.character(net$target)
# keep the interactions associated with sources and targets of interest
if (!is.null(sources.use)){
if (is.numeric(sources.use)) {
sources.use <- cell.levels[sources.use]
}
net <- subset(net, source %in% sources.use)
}
if (!is.null(targets.use)){
if (is.numeric(targets.use)) {
targets.use <- cell.levels[targets.use]
}
net <- subset(net, target %in% targets.use)
}
# remove the interactions with zero values
net <- subset(net, prob > 0)
# create a fake data if keeping the cell types (i.e., sectors) without any interactions
if (!remove.isolate) {
cells.removed <- setdiff(cell.levels, as.character(union(net$source,net$target)))
if (length(cells.removed) > 0) {
net.fake <- data.frame(cells.removed, cells.removed, 1e-10*sample(length(cells.removed), length(cells.removed)))
colnames(net.fake) <- colnames(net)
net <- rbind(net, net.fake)
link.visible <- net[, 1:2]
link.visible$plot <- FALSE
link.visible$plot[1:(nrow(net) - nrow(net.fake))] <- TRUE
# directional <- net[, 1:2]
# directional$plot <- 0
# directional$plot[1:(nrow(net) - nrow(net.fake))] <- 1
# link.arr.type = "big.arrow"
# message("Set scale = TRUE when remove.isolate = FALSE")
scale = TRUE
}
}
df <- net
cells.use <- union(df$source,df$target)
# define grid order
order.sector <- cell.levels[cell.levels %in% cells.use]
# define grid color
if (is.null(color.use)){
color.use = scPalette(length(cell.levels))
names(color.use) <- cell.levels
} else if (is.null(names(color.use))) {
names(color.use) <- cell.levels
}
grid.col <- color.use[order.sector]
names(grid.col) <- order.sector
# set grouping information
if (!is.null(group)) {
group <- group[names(group) %in% order.sector]
}
# define edge color
edge.color <- color.use[as.character(df$source)]
if (directional == 0 | directional == 2) {
link.arr.type = "triangle"
} else {
link.arr.type = "big.arrow"
}
circos.clear()
chordDiagram(df,
order = order.sector,
col = edge.color,
grid.col = grid.col,
transparency = transparency,
link.border = link.border,
directional = directional,
direction.type = c("diffHeight","arrows"),
link.arr.type = link.arr.type, # link.border = "white",
annotationTrack = "grid",
annotationTrackHeight = annotationTrackHeight,
preAllocateTracks = list(track.height = max(strwidth(order.sector))),
small.gap = small.gap,
big.gap = big.gap,
link.visible = link.visible,
scale = scale,
group = group,
link.target.prop = link.target.prop,
reduce = reduce,
...)
circos.track(track.index = 1, panel.fun = function(x, y) {
xlim = get.cell.meta.data("xlim")
xplot = get.cell.meta.data("xplot")
ylim = get.cell.meta.data("ylim")
sector.name = get.cell.meta.data("sector.index")
circos.text(mean(xlim), ylim[1], sector.name, facing = "bending", niceFacing = TRUE, adj = c(0, 0.5),cex = lab.cex)
}, bg.border = NA)
# https://jokergoo.github.io/circlize_book/book/legends.html
if (show.legend) {
lgd <- ComplexHeatmap::Legend(at = names(grid.col), type = "grid", legend_gp = grid::gpar(fill = grid.col), title = "Cell State")
ComplexHeatmap::draw(lgd, x = unit(1, "npc")-unit(legend.pos.x, "mm"), y = unit(legend.pos.y, "mm"), just = c("right", "bottom"))
}
if(!is.null(title.name)){
# title(title.name, cex = 1)
text(-0, 1.02, title.name, cex=1)
}
circos.clear()
gg <- recordPlot()
return(gg)
}
netVisual_chord_gene <- function(object, slot.name = "net", color.use = NULL,
signaling = NULL, pairLR.use = NULL, net = NULL,
sources.use = NULL, targets.use = NULL,
lab.cex = 0.8,small.gap = 1, big.gap = 10, annotationTrackHeight = c(0.03),
link.visible = TRUE, scale = FALSE, directional = 1, link.target.prop = TRUE, reduce = -1,
transparency = 0.4, link.border = NA,
title.name = NULL, legend.pos.x = 20, legend.pos.y = 20, show.legend = TRUE,
thresh = 0.05,
...){
if (!is.null(pairLR.use)) {
if (!is.data.frame(pairLR.use)) {
stop("pairLR.use should be a data frame with a signle column named either 'interaction_name' or 'pathway_name' ")
} else if ("pathway_name" %in% colnames(pairLR.use)) {
message("slot.name is set to be 'netP' when pairLR.use contains signaling pathways")
slot.name = "netP"
}
}
if (!is.null(pairLR.use) & !is.null(signaling)) {
stop("Please do not assign values to 'signaling' when using 'pairLR.use'")
}
if (is.null(net)) {
prob <- slot(object, "net")$prob
pval <- slot(object, "net")$pval
prob[pval > thresh] <- 0
net <- reshape2::melt(prob, value.name = "prob")
colnames(net)[1:3] <- c("source","target","interaction_name")
pairLR = dplyr::select(object@LR$LRsig, c("interaction_name_2", "pathway_name", "ligand", "receptor" ,"annotation","evidence"))
idx <- match(net$interaction_name, rownames(pairLR))
temp <- pairLR[idx,]
net <- cbind(net, temp)
}
if (!is.null(signaling)) {
pairLR.use <- data.frame()
for (i in 1:length(signaling)) {
pairLR.use.i <- searchPair(signaling = signaling[i], pairLR.use = object@LR$LRsig, key = "pathway_name", matching.exact = T, pair.only = T)
pairLR.use <- rbind(pairLR.use, pairLR.use.i)
}
}
if (!is.null(pairLR.use)){
if ("interaction_name" %in% colnames(pairLR.use)) {
net <- subset(net,interaction_name %in% pairLR.use$interaction_name)
} else if ("pathway_name" %in% colnames(pairLR.use)) {
net <- subset(net, pathway_name %in% as.character(pairLR.use$pathway_name))
}
}
if (slot.name == "netP") {
net <- dplyr::select(net, c("source","target","pathway_name","prob"))
net$source_target <- paste(net$source, net$target, sep = "sourceTotarget")
net <- net %>% dplyr::group_by(source_target, pathway_name) %>% dplyr::summarize(prob = sum(prob))
a <- stringr::str_split(net$source_target, "sourceTotarget", simplify = T)
net$source <- as.character(a[, 1])
net$target <- as.character(a[, 2])
net$ligand <- net$pathway_name
net$receptor <- " "
}
# keep the interactions associated with sources and targets of interest
if (!is.null(sources.use)){
if (is.numeric(sources.use)) {
sources.use <- levels(object@idents)[sources.use]
}
net <- subset(net, source %in% sources.use)
} else {
sources.use <- levels(object@idents)
}
if (!is.null(targets.use)){
if (is.numeric(targets.use)) {
targets.use <- levels(object@idents)[targets.use]
}
net <- subset(net, target %in% targets.use)
} else {
targets.use <- levels(object@idents)
}
# remove the interactions with zero values
df <- subset(net, prob > 0)
if (nrow(df) == 0) {
stop("No signaling links are inferred! ")
}
if (length(unique(net$ligand)) == 1) {
message("You may try the function `netVisual_chord_cell` for visualizing individual signaling pathway")
}
df$id <- 1:nrow(df)
# deal with duplicated sector names
ligand.uni <- unique(df$ligand)
for (i in 1:length(ligand.uni)) {
df.i <- df[df$ligand == ligand.uni[i], ]
source.uni <- unique(df.i$source)
for (j in 1:length(source.uni)) {
df.i.j <- df.i[df.i$source == source.uni[j], ]
df.i.j$ligand <- paste0(df.i.j$ligand, paste(rep(' ',j-1),collapse = ''))
df$ligand[df$id %in% df.i.j$id] <- df.i.j$ligand
}
}
receptor.uni <- unique(df$receptor)
for (i in 1:length(receptor.uni)) {
df.i <- df[df$receptor == receptor.uni[i], ]
target.uni <- unique(df.i$target)
for (j in 1:length(target.uni)) {
df.i.j <- df.i[df.i$target == target.uni[j], ]
df.i.j$receptor <- paste0(df.i.j$receptor, paste(rep(' ',j-1),collapse = ''))
df$receptor[df$id %in% df.i.j$id] <- df.i.j$receptor
}
}
cell.order.sources <- levels(object@idents)[levels(object@idents) %in% sources.use]
cell.order.targets <- levels(object@idents)[levels(object@idents) %in% targets.use]
df$source <- factor(df$source, levels = cell.order.sources)
df$target <- factor(df$target, levels = cell.order.targets)
# df.ordered.source <- df[with(df, order(source, target, -prob)), ]
# df.ordered.target <- df[with(df, order(target, source, -prob)), ]
df.ordered.source <- df[with(df, order(source, -prob)), ]
df.ordered.target <- df[with(df, order(target, -prob)), ]
order.source <- unique(df.ordered.source[ ,c('ligand','source')])
order.target <- unique(df.ordered.target[ ,c('receptor','target')])
# define sector order
order.sector <- c(order.source$ligand, order.target$receptor)
# define cell type color
if (is.null(color.use)){
color.use = scPalette(nlevels(object@idents))
names(color.use) <- levels(object@idents)
color.use <- color.use[levels(object@idents) %in% as.character(union(df$source,df$target))]
} else if (is.null(names(color.use))) {
names(color.use) <- levels(object@idents)
color.use <- color.use[levels(object@idents) %in% as.character(union(df$source,df$target))]
}
# define edge color
edge.color <- color.use[as.character(df.ordered.source$source)]
names(edge.color) <- as.character(df.ordered.source$source)
# define grid colors
grid.col.ligand <- color.use[as.character(order.source$source)]
names(grid.col.ligand) <- as.character(order.source$source)
grid.col.receptor <- color.use[as.character(order.target$target)]
names(grid.col.receptor) <- as.character(order.target$target)
grid.col <- c(as.character(grid.col.ligand), as.character(grid.col.receptor))
names(grid.col) <- order.sector
df.plot <- df.ordered.source[ ,c('ligand','receptor','prob')]
if (directional == 2) {
link.arr.type = "triangle"
} else {
link.arr.type = "big.arrow"
}
circos.clear()
chordDiagram(df.plot,
order = order.sector,
col = edge.color,
grid.col = grid.col,
transparency = transparency,
link.border = link.border,
directional = directional,
direction.type = c("diffHeight","arrows"),
link.arr.type = link.arr.type,
annotationTrack = "grid",
annotationTrackHeight = annotationTrackHeight,
preAllocateTracks = list(track.height = max(strwidth(order.sector))),
small.gap = small.gap,
big.gap = big.gap,
link.visible = link.visible,
scale = scale,
link.target.prop = link.target.prop,
reduce = reduce,
...)
circos.track(track.index = 1, panel.fun = function(x, y) {
xlim = get.cell.meta.data("xlim")
xplot = get.cell.meta.data("xplot")
ylim = get.cell.meta.data("ylim")
sector.name = get.cell.meta.data("sector.index")
circos.text(mean(xlim), ylim[1], sector.name, facing = "bending", niceFacing = TRUE, adj=c(0.5,0.01),cex = lab.cex)
}, bg.border = NA)
# https://jokergoo.github.io/circlize_book/book/legends.html
if (show.legend) {
lgd <- ComplexHeatmap::Legend(at = names(color.use), type = "grid", legend_gp = grid::gpar(fill = color.use), title = "Cell State")
ComplexHeatmap::draw(lgd, x = unit(1, "npc")-unit(legend.pos.x, "mm"), y = unit(legend.pos.y, "mm"), just = c("right", "bottom"))
}
circos.clear()
if(!is.null(title.name)){
text(-0, 1.02, title.name, cex=1)
}
gg <- recordPlot()
return(gg)
}netVisual_chord_gene(cellchat, signaling = c("CCL"),
#sources.use = c(1,2,3,5,6,7,8), targets.use = c(1,2,3,5,6,7,8),
legend.pos.x = 8,scale=TRUE, color.use = c('#CD6600', '#FF8C00', '#CD00CD', #'#388E8E',
'#80007c', '#8FBC8F', '#C1FFC1', '#228B22'),lab.cex = 0.8) a <- netVisual_chord_gene(cellchat, signaling = c("CCL"),
#sources.use = c(1,2,3,5,6,7,8), targets.use = c(1,2,3,5,6,7,8),
legend.pos.x = 8,scale=TRUE, color.use = c('#CD6600', '#FF8C00', '#CD00CD', #'#388E8E',
'#BF8FAF', '#8FBC8F', '#C1FFC1', '#228B22'),lab.cex = 1) jpeg("final_figures/CXCL_chord.jpeg",width=30,height=30,units="cm",quality=100,res=300)
netVisual_chord_gene(cellchat, signaling = c("CXCL"),
#sources.use = c(1,2,3,5,6,7,8), targets.use = c(1,2,3,5,6,7,8),
legend.pos.x = 8,scale=TRUE, color.use = c('#CD6600', '#FF8C00', '#CD00CD', #'#388E8E',
'#BC8F8F', '#8FBC8F', '#C1FFC1', '#228B22'),lab.cex = 3)
dev.off()---
title: "Cellchat script"
date: "15/05/23"
author: "Amanda Oliver"
output: html_notebook
---

Preprocessing in python: briefly I normalised and log transformed the raw counts data, filtered for celltypes/conditions of interest and export the normalised counts as csv (same as cellphoneDB), also export metadata for cell type (level_3_annot).

Install CellChat (https://github.com/sqjin/CellChat)
```{r}
#devtools::install_github("jokergoo/ComplexHeatmap")
#devtools::install_github("jokergoo/circlize")
#devtools::install_github("sqjin/CellChat")
```

Install other dependencies
```{r}
#install.packages('NMF')
```

Load required libraries
```{r}
library(CellChat)
library(patchwork)
library(Seurat)
library(SeuratObject)
library(plyr)
options(stringsAsFactors = FALSE)

library(NMF)
library(dplyr)
#library(sceasy)
library(igraph)
library(Matrix)
library(ggplot2)
library(ggalluvial)
library(circlize)
```

Load input data and meta data
```{r}
data <- read.table(file = '/nfs/team205/ao15/Megagut/Annotations_v3/disease_analysis/interactions/counts/pooled_disease.remapped.allgenes.AP_SI.counts.csv', header = T, row.names=1, sep=",", as.is=T)
head(data[,1:10])
meta <- read.csv(file = '/nfs/team205/ao15/Megagut/Annotations_v3/disease_analysis/interactions/meta/pooled_disease.remapped.allgenes.AP_SI.meta.csv', header = T, row.names=1,sep=",", as.is=T)

```

```{r}
head(data[,1:10])
```

```{r}
head(meta)
```


```{r}
#to make the barcodes in counts consistent, for some reason the barcodes that read "AACCGCGCAGTTTACG-HCA_A_GT12934998" are being changed to "AACCGCGCAGTTTACG.HCA_A_GT12934998"
colnames(data) = gsub('[.]', '-', colnames(data))
#head(data[,1:10])

#quickly list barcodes by transposing dataframe
#data_t <- t(data)
#row.names(data_t)
head(data[,1:10])
```

Make seurat object from csv
```{r}
srat <- CreateSeuratObject(data, project = "MUC6", assay = "RNA", min.cells = 0, min.features = 0, meta.data = meta)
```

From here the script follows the CellChat tutorial
```{r}
data.input <- srat@assays$RNA@counts

cell.use = rownames(meta)
```


Create a cellchat object:
```{r}
cellchat <- createCellChat(object = data.input, meta = meta, group.by = "level_3_annot")
```

```{r}
cellchat <- addMeta(cellchat, meta = meta)
cellchat <- setIdent(cellchat, ident.use = "level_3_annot") # set "labels" as default cell identity
levels(cellchat@idents) # show factor levels of the cell labels
groupSize <- as.numeric(table(cellchat@idents)) # number of cells in each cell group
```

```{r}
CellChatDB <- CellChatDB.human # use CellChatDB.mouse if running on mouse data
showDatabaseCategory(CellChatDB)
```

```{r}
# Show the structure of the database
dplyr::glimpse(CellChatDB$interaction)
#> Rows: 1,939
#> Columns: 11
#> $ interaction_name   <chr> "TGFB1_TGFBR1_TGFBR2", "TGFB2_TGFBR1_TGFBR2", "TGF…
#> $ pathway_name       <chr> "TGFb", "TGFb", "TGFb", "TGFb", "TGFb", "TGFb", "T…
#> $ ligand             <chr> "TGFB1", "TGFB2", "TGFB3", "TGFB1", "TGFB1", "TGFB…
#> $ receptor           <chr> "TGFbR1_R2", "TGFbR1_R2", "TGFbR1_R2", "ACVR1B_TGF…
#> $ agonist            <chr> "TGFb agonist", "TGFb agonist", "TGFb agonist", "T…
#> $ antagonist         <chr> "TGFb antagonist", "TGFb antagonist", "TGFb antago…
#> $ co_A_receptor      <chr> "", "", "", "", "", "", "", "", "", "", "", "", ""…
#> $ co_I_receptor      <chr> "TGFb inhibition receptor", "TGFb inhibition recep…
#> $ evidence           <chr> "KEGG: hsa04350", "KEGG: hsa04350", "KEGG: hsa0435…
#> $ annotation         <chr> "Secreted Signaling", "Secreted Signaling", "Secre…
#> $ interaction_name_2 <chr> "TGFB1 - (TGFBR1+TGFBR2)", "TGFB2 - (TGFBR1+TGFBR2…

# use a subset of CellChatDB for cell-cell communication analysis
#CellChatDB.use <- subsetDB(CellChatDB, search = "Secreted Signaling") # use Secreted Signaling
# use all CellChatDB for cell-cell communication analysis
CellChatDB.use <- CellChatDB # simply use the default CellChatDB

# set the used database in the object
cellchat@DB <- CellChatDB.use
```

```{r}
cellchat <- subsetData(cellchat) # subset the expression data of signaling genes for saving computation cost
future::plan("multiprocess", workers = 4) # do parallel
```

```{r}
cellchat <- identifyOverExpressedGenes(cellchat)
cellchat <- identifyOverExpressedInteractions(cellchat)
cellchat <- projectData(cellchat, PPI.human)
```

```{r}
#note trim is referring to Cellchats method for calculating the truncated mean, default is 0.25 meaning expression will be set to 0 if 25% or less of cells of a given cell type express the ligand/receptor. By default, I've changed this to 10% to account for genes expressed in a small portion of cells, that could still be biologically relevant. I beleive this is more similar to CellphoneDB cut offs anyway.

cellchat <- computeCommunProb(cellchat, raw.use = TRUE, type = "truncatedMean", trim = 0.1)
# Filter out the cell-cell communication if there are only few number of cells in certain cell groups
cellchat <- filterCommunication(cellchat, min.cells = 10)
```



Export results as a data frame
```{r}
df.net <- subsetCommunication(cellchat)
head(df.net)
write.csv(df.net, "/nfs/team205/ao15/Megagut/Annotations_v3/disease_analysis/interactions/pooled_disease.remapped.allgenes.AP_SI.cellchat_output_230523.csv", row.names = TRUE)
```

```{r}
cellchat <- computeCommunProbPathway(cellchat)
cellchat <- aggregateNet(cellchat)
```

```{r}
groupSize <- as.numeric(table(cellchat@idents))
par(mfrow = c(1,2), xpd=TRUE)
netVisual_circle(cellchat@net$count, vertex.weight = groupSize, weight.scale = T, label.edge= F, title.name = "Number of interactions",vertex.label.cex = 0.5)
netVisual_circle(cellchat@net$weight, vertex.weight = groupSize, weight.scale = T, label.edge= F, title.name = "Interaction weights/strength", vertex.label.cex = 0.5)
```
```{r}
# Compute the network centrality scores
cellchat <- netAnalysis_computeCentrality(cellchat, slot.name = "netP") # the slot 'netP' means the inferred intercellular communication network of signaling pathways
# Visualize the computed centrality scores using heatmap, allowing ready identification of major signaling roles of cell groups
#netAnalysis_signalingRole_network(cellchat, signaling = pathways.show, width = 8, height = 2.5, font.size = 10)
```

```{r}
# Signaling role analysis on the aggregated cell-cell communication network from all signaling pathways
ht1 <- netAnalysis_signalingRole_heatmap(cellchat, pattern = "outgoing", width = 5, height = 10, font.size = 3)
ht2 <- netAnalysis_signalingRole_heatmap(cellchat, pattern = "incoming", width = 5, height = 10, font.size = 3)
pdf(file ="all_genes_plots/diseaseonly/cellchat_signalling_heatmap.pdf", width = 10, height =20)
ht1 + ht2
dev.off()
#personally I find this plot one of the most useful to look at an overview of signaling pathways, and then use the other functions to plot them in whichever way makes sense
```

```{r}
#save cellchat object
saveRDS(cellchat, file = "/nfs/team205/ao15/Megagut/Annotations_v3/disease_analysis/interactions/pooled_disease.remapped.allgenes.AP_SI.cellchat_object.rds")
```


```{r}
# Signaling role analysis on the aggregated cell-cell communication network from all signaling pathways
gg1 <- netAnalysis_signalingRole_scatter(cellchat)
levels(cellchat@idents)
gg1
#this plot is helpful to look at the overall signaling roles of cell types (ie. are they mostly sending signals, receiving signals or a mixture of both)
```
```{r fig.height= 10, fig.width=10}
mat <- cellchat@net$weight
par(mfrow = c(2,2), xpd=TRUE)
for (i in 1:nrow(mat)) {
  mat2 <- matrix(0, nrow = nrow(mat), ncol = ncol(mat), dimnames = dimnames(mat))
  mat2[i, ] <- mat[i, ]
  netVisual_circle(mat2, vertex.weight = groupSize, weight.scale = T, edge.weight.max = max(mat), title.name = rownames(mat)[i])
}
```

```{r}
cellchat@netP$pathways
```


```{r fig.height=10}
pathways.show <- c("MHC-II") 
# Hierarchy plot
# Here we define `vertex.receive` so that the left portion of the hierarchy plot shows signaling to fibroblast and the right portion shows signaling to immune cells 
vertex.receiver = seq(4,8) # a numeric vector. 
netVisual_aggregate(cellchat, signaling = pathways.show,  vertex.receiver = vertex.receiver)
```

```{r fig.height=15, fig.width=22}
pathways.show <- c("CCL") 
# Heatmap
par(mfrow=c(1,1))
pdf(file ="all_genes_plots/diseaseonly/cellchat_CCLheatmap.pdf", width = 20, height =16)
netVisual_heatmap(cellchat, signaling = pathways.show, color.heatmap = "Reds",width=50,height=50,font.size=6)
```
```{r fig.height=15, fig.width=22}
pathways.show <- c("CXCL") 
# Heatmap
par(mfrow=c(1,1))
pdf(file ="all_genes_plots/diseaseonly/cellchat_CXCLheatmap.pdf", width = 20, height =16)
netVisual_heatmap(cellchat, signaling = pathways.show, color.heatmap = "Reds",width=50,height=50,font.size=6)
```

```{r fig.height=15, fig.width=22}
pathways.show <- c("MHC-II") 
# Heatmap
par(mfrow=c(1,1))
pdf(file ="all_genes_plots/diseaseonly/cellchat_MHCIIheatmap.pdf", width = 20, height =16)
netVisual_heatmap(cellchat, signaling = pathways.show, color.heatmap = "Reds",width=50,height=50,font.size=4)
```



```{r fig.height=8, fig.width=8}
par(mfrow=c(1,1))
pdf(file ="all_genes_plots/diseaseonly/cellchat_CCLchord.pdf", width = 20, height =16)
netVisual_aggregate(cellchat, signaling = 'CCL', layout = "chord", remove.isolate = TRUE, scale=TRUE)
dev.off()
```


```{r fig.height=8, fig.width=8}
par(mfrow=c(1,1))
pdf(file ="all_genes_plots/diseaseonly/cellchat_CXCLchord.pdf", width = 20, height =16)
netVisual_aggregate(cellchat, signaling = 'CXCL', layout = "chord", remove.isolate = TRUE, scale=TRUE)
dev.off()
```

```{r fig.height=8, fig.width=8}
par(mfrow=c(1,1))
pdf(file ="all_genes_plots/diseaseonly/cellchat_MHCIIchord.pdf", width = 20, height =16)
netVisual_aggregate(cellchat, signaling = 'MHC-II', layout = "chord", remove.isolate = TRUE, scale=TRUE)
dev.off()
```


```{r fig.height=15, fig.width=15}
# Chord diagram
#group.cellType <- c(rep("Fibro", 2), rep("B", 3), rep("SMG", 3)) # grouping cell clusters into fibroblast, DC and TC cells
#names(group.cellType) <- levels(cellchat@idents)
#netVisual_chord_cell(cellchat, signaling = pathways.show, group = group.cellType, title.name = paste0(pathways.show, " signaling network"))
netVisual_chord_cell(cellchat, signaling = pathways.show, title.name = paste0(pathways.show, " signaling network"))
```

```{r}
netAnalysis_contribution(cellchat, signaling = "CXCL")
#to get a cursory look at which L-R pairs within a given network, can also look at the full overview on the cellchat database http://www.cellchat.org/cellchatdb/
```


```{r}
netAnalysis_contribution(cellchat, signaling = "CCL")
#to get a cursory look at which L-R pairs within a given network, can also look at the full overview on the cellchat database http://www.cellchat.org/cellchatdb/
```


```{r}
netAnalysis_contribution(cellchat, signaling = "MHC-II")
#to get a cursory look at which L-R pairs within a given network, can also look at the full overview on the cellchat database http://www.cellchat.org/cellchatdb/
```
```{r}
netAnalysis_contribution(cellchat, signaling = "THBS")
#to get a cursory look at which L-R pairs within a given network, can also look at the full overview on the cellchat database http://www.cellchat.org/cellchatdb/
```

```{r}
pathways.show = "CCL"
pairLR.CCL <- extractEnrichedLR(cellchat, signaling = pathways.show, geneLR.return = FALSE)
LR.show <- pairLR.CCL[6,]
pdf(file ="all_genes_plots/diseaseonly/cellchat_CCLnetvis_individual.pdf", width = 20, height =16)
netVisual_individual(cellchat, signaling = pathways.show, pairLR.use = LR.show, layout = "chord")

```

```{r}
netVisual_aggregate(cellchat, signaling = 'CXCL', layout = "circle")
```

```{r}
netVisual_aggregate(cellchat, signaling = 'SAA', layout = "circle")
```

```{r fig.height=30}
netVisual_bubble(cellchat, sources.use = 57, remove.isolate = FALSE)
```



```{r fig.height=30}
pdf(file ="all_genes_plots/diseaseonly/cellchat_MUC6source_netvisualbubble.pdf", width = 20, height =30)
netVisual_bubble(cellchat, sources.use = 57, remove.isolate = FALSE)
```
```{r fig.height=30}
pdf(file ="all_genes_plots/diseaseonly/cellchat_MUC6target_netvisualbubble.pdf", width = 20, height =30)
netVisual_bubble(cellchat, targets.use = 57, remove.isolate = FALSE)
```

```{r fig.height=40}
pdf(file ="all_genes_plots/diseaseonly/cellchat_OralFibrosource_netvisualbubble.pdf", width = 20, height =40)
netVisual_bubble(cellchat, sources.use = 63, remove.isolate = FALSE)
```
haven't run any of this yet -->


```{r}
netVisual_chord_gene <- function(object, slot.name = "net", color.use = NULL,
                                 signaling = NULL, pairLR.use = NULL, net = NULL,
                                 sources.use = NULL, targets.use = NULL,
                                 lab.cex = 0.8,small.gap = 1, big.gap = 10, annotationTrackHeight = c(0.03),
                                 link.visible = TRUE, scale = FALSE, directional = 1, link.target.prop = TRUE, reduce = -1,
                                 transparency = 0.4, link.border = NA,
                                 title.name = NULL, legend.pos.x = 20, legend.pos.y = 20, show.legend = TRUE,
                                 thresh = 0.05,
                                 ...){
  if (!is.null(pairLR.use)) {
    if (!is.data.frame(pairLR.use)) {
      stop("pairLR.use should be a data frame with a signle column named either 'interaction_name' or 'pathway_name' ")
    } else if ("pathway_name" %in% colnames(pairLR.use)) {
      message("slot.name is set to be 'netP' when pairLR.use contains signaling pathways")
      slot.name = "netP"
    }
  }

  if (!is.null(pairLR.use) & !is.null(signaling)) {
    stop("Please do not assign values to 'signaling' when using 'pairLR.use'")
  }

  if (is.null(net)) {
    prob <- slot(object, "net")$prob
    pval <- slot(object, "net")$pval
    prob[pval > thresh] <- 0
    net <- reshape2::melt(prob, value.name = "prob")
    colnames(net)[1:3] <- c("source","target","interaction_name")

    pairLR = dplyr::select(object@LR$LRsig, c("interaction_name_2", "pathway_name",  "ligand",  "receptor" ,"annotation","evidence"))
    idx <- match(net$interaction_name, rownames(pairLR))
    temp <- pairLR[idx,]
    net <- cbind(net, temp)
  }

  if (!is.null(signaling)) {
    pairLR.use <- data.frame()
    for (i in 1:length(signaling)) {
      pairLR.use.i <- searchPair(signaling = signaling[i], pairLR.use = object@LR$LRsig, key = "pathway_name", matching.exact = T, pair.only = T)
      pairLR.use <- rbind(pairLR.use, pairLR.use.i)
    }
  }

  if (!is.null(pairLR.use)){
    if ("interaction_name" %in% colnames(pairLR.use)) {
      net <- subset(net,interaction_name %in% pairLR.use$interaction_name)
    } else if ("pathway_name" %in% colnames(pairLR.use)) {
      net <- subset(net, pathway_name %in% as.character(pairLR.use$pathway_name))
    }
  }

  if (slot.name == "netP") {
    net <- dplyr::select(net, c("source","target","pathway_name","prob"))
    net$source_target <- paste(net$source, net$target, sep = "sourceTotarget")
    net <- net %>% dplyr::group_by(source_target, pathway_name) %>% dplyr::summarize(prob = sum(prob))
    a <- stringr::str_split(net$source_target, "sourceTotarget", simplify = T)
    net$source <- as.character(a[, 1])
    net$target <- as.character(a[, 2])
    net$ligand <- net$pathway_name
    net$receptor <- " "
  }

  # keep the interactions associated with sources and targets of interest
  if (!is.null(sources.use)){
    if (is.numeric(sources.use)) {
      sources.use <- levels(object@idents)[sources.use]
    }
    net <- subset(net, source %in% sources.use)
  } else {
    sources.use <- levels(object@idents)
  }
  if (!is.null(targets.use)){
    if (is.numeric(targets.use)) {
      targets.use <- levels(object@idents)[targets.use]
    }
    net <- subset(net, target %in% targets.use)
  } else {
    targets.use <- levels(object@idents)
  }
  # remove the interactions with zero values
  df <- subset(net, prob > 0)

  if (nrow(df) == 0) {
    stop("No signaling links are inferred! ")
  }

  if (length(unique(net$ligand)) == 1) {
    message("You may try the function `netVisual_chord_cell` for visualizing individual signaling pathway")
  }

  df$id <- 1:nrow(df)
  # deal with duplicated sector names
  ligand.uni <- unique(df$ligand)
  for (i in 1:length(ligand.uni)) {
    df.i <- df[df$ligand == ligand.uni[i], ]
    source.uni <- unique(df.i$source)
    for (j in 1:length(source.uni)) {
      df.i.j <- df.i[df.i$source == source.uni[j], ]
      df.i.j$ligand <- paste0(df.i.j$ligand, paste(rep(' ',j-1),collapse = ''))
      df$ligand[df$id %in% df.i.j$id] <- df.i.j$ligand
    }
  }
  receptor.uni <- unique(df$receptor)
  for (i in 1:length(receptor.uni)) {
    df.i <- df[df$receptor == receptor.uni[i], ]
    target.uni <- unique(df.i$target)
    for (j in 1:length(target.uni)) {
      df.i.j <- df.i[df.i$target == target.uni[j], ]
      df.i.j$receptor <- paste0(df.i.j$receptor, paste(rep(' ',j-1),collapse = ''))
      df$receptor[df$id %in% df.i.j$id] <- df.i.j$receptor
    }
  }

  cell.order.sources <- levels(object@idents)[levels(object@idents) %in% sources.use]
  cell.order.targets <- levels(object@idents)[levels(object@idents) %in% targets.use]

  df$source <- factor(df$source, levels = cell.order.sources)
  df$target <- factor(df$target, levels = cell.order.targets)
  # df.ordered.source <- df[with(df, order(source, target, -prob)), ]
  # df.ordered.target <- df[with(df, order(target, source, -prob)), ]
  df.ordered.source <- df[with(df, order(source, -prob)), ]
  df.ordered.target <- df[with(df, order(target, -prob)), ]

  order.source <- unique(df.ordered.source[ ,c('ligand','source')])
  order.target <- unique(df.ordered.target[ ,c('receptor','target')])

  # define sector order
  order.sector <- c(order.source$ligand, order.target$receptor)

  # define cell type color
  if (is.null(color.use)){
    color.use = scPalette(nlevels(object@idents))
    names(color.use) <- levels(object@idents)
    color.use <- color.use[levels(object@idents) %in% as.character(union(df$source,df$target))]
  } else if (is.null(names(color.use))) {
    names(color.use) <- levels(object@idents)
    color.use <- color.use[levels(object@idents) %in% as.character(union(df$source,df$target))]
  }

  # define edge color
  edge.color <- color.use[as.character(df.ordered.source$source)]
  names(edge.color) <- as.character(df.ordered.source$source)

  # define grid colors
  grid.col.ligand <- color.use[as.character(order.source$source)]
  names(grid.col.ligand) <- as.character(order.source$source)
  grid.col.receptor <- color.use[as.character(order.target$target)]
  names(grid.col.receptor) <- as.character(order.target$target)
  grid.col <- c(as.character(grid.col.ligand), as.character(grid.col.receptor))
  names(grid.col) <- order.sector

  df.plot <- df.ordered.source[ ,c('ligand','receptor','prob')]

  if (directional == 2) {
    link.arr.type = "triangle"
  } else {
    link.arr.type = "big.arrow"
  }
  circos.clear()
  chordDiagram(df.plot,
               order = order.sector,
               col = edge.color,
               grid.col = grid.col,
               transparency = transparency,
               link.border = link.border,
               directional = directional,
               direction.type = c("diffHeight","arrows"),
               link.arr.type = link.arr.type,
               annotationTrack = "grid",
               annotationTrackHeight = annotationTrackHeight,
               preAllocateTracks = list(track.height = max(strwidth(order.sector))),
               small.gap = small.gap,
               big.gap = big.gap,
               link.visible = link.visible,
               scale = scale,
               link.target.prop = link.target.prop,
               reduce = reduce,
               ...)

  circos.track(track.index = 1, panel.fun = function(x, y) {
    xlim = get.cell.meta.data("xlim")
    xplot = get.cell.meta.data("xplot")
    ylim = get.cell.meta.data("ylim")
    sector.name = get.cell.meta.data("sector.index")
    circos.text(mean(xlim), ylim[1], sector.name, facing = "bending", niceFacing = TRUE, adj=c(0.5,0.01),cex = lab.cex)
  }, bg.border = NA)


  # https://jokergoo.github.io/circlize_book/book/legends.html
  if (show.legend) {
    lgd <- ComplexHeatmap::Legend(at = names(color.use), type = "grid", legend_gp = grid::gpar(fill = color.use), title = "Cell State")
    ComplexHeatmap::draw(lgd, x = unit(1, "npc")-unit(legend.pos.x, "mm"), y = unit(legend.pos.y, "mm"), just = c("right", "bottom"))
  }

  circos.clear()
  if(!is.null(title.name)){
    text(-0, 1.02, title.name, cex=1)
  }
  gg <- recordPlot()
  return(gg)
}
```

```{r fig.height=15,fig.width=15}
netVisual_chord_gene(cellchat, signaling = c("SAA"),legend.pos.x = 8, scale=FALSE)#, sources.use = c(1,2,3,4,5,6,7), targets.use = c(1,2,3,4,5,6,7), lab.cex=1,  color.use = c('#CD6600', '#FF8C00', '#CD00CD','#0000EE', '#8FBC8F','#C1FFC1','#228B22')) 
```
```{r}
colours = c('#db9602',#   'B_GC_I':
 '#e2d138',#   'B_GC_I':
'#a33c22',#   'B_memory':
  '#9b0319',# 'B_naive':
  '#f76c56',# 'B_plasma_IgA1':
  '#d6558d',# 'B_plasma_IgA2':
'#632f17',#   'B_plasma_IgG':
  '#c66d31',# 'B_plasma_IgM':
  '#9e53db',# 'B_plasmablast':
  '#8a4682',# 'B_preB':
'#d34794',#   'B_proB':
'#39997c',#   'BEST4_enterocyte_colonocyte':
'#bd7879',#   'Crypt_fibroblast_PI16':
'#8c543f',#   'DC_cDC1':
'#cfdb65',#   'DC_cDC2':
'#c7a642',#   'DC_langerhans':
'#e6a519',#   'DC_migratory':
'#bdb197',#   'DC_pDC':
'#fa6e6e',#   'EC_arterial_1':
'#ca6092',#   'EC_arterial_2':
'#855f9a',#   'EC_capillary':
'#fac06e',#   'EC_cycling':
'#999999',#   'EC_lymphatic':
'#2a4858',# 'EC_venous':
'#DBA507',#   'Enteric_neural_crest_cycling':
'#e1b5e6',#     'Enterocyte':
'#68b7fc',#   'Enteroendocrine':
  '#8b4eba',# 'Eosinophil/basophil':
  '#c924b9',# 'Epithelial_stem':
  '#0e539c',# 'Erythrocytes':
  '#f0c134',# 'Fibroblast_reticular':
'#f0982c',#   'Follicular_DC':
'#3fafb5',#   'gdT':
'#26daf2',#     'gdT_naive':
'#8EC7D2',#   'Glial_1':
'#0D6986',#   'Glial_2':
'#053240',#   'Glial_3':
'#a8c545',#   'Glial/Enteric_neural_crest':
'#6c9939',#     'Goblet':
'#d1d14f',# 'Goblet_cycling':
'#e9f7ad',# 'Goblet_progenitor':
'#778c00',#     'ILC3':
'#AAC789',#   'Immune_recruiting_pericyte':
'#e95e50',#   'Lamina_propria_fibroblast_ADAMDEC1':
'#486626',# 'Macrophage':
'#caf9cf',#   'Macrophage_CD5L':
'#8fd9d0',#   'Macrophage_LYVE1':
'#a5f002',#   'Macrophage_MMP9':
'#42c7ac',#   'Macrophage_TREM2':
'#21b796',#     'MAIT':
'#826e91',#   'Mast':
'#c730aa',#   'Megakaryocyte/platelet':
'#8F6592',#   'Mesothelium':
'#e55b85',#   'Microfold':
'#2a497a',#   'Mono/neutrophil_MPO':
'#5baf07',#   'Monocyte':
'#f7b37c',#   'Mucous_gland_neck':
'#CCAE91',#   'Myofibroblast':
'#c50637',#   'Neuroblast':
'#0c1e0e',#   'NK_CD16':
'#3f8c08',#   'NK_CD56bright':
'#63A0C0',#     'Oesophagus_fibroblast':
'#303267',#   'Oral_mucosa_fibroblast':
'#79508f',#   'Paneth':
'#437356',#     'Pericyte':
'#522e25',#   'Rectum_fibroblast':
'#d9b74a',#   'Surface_foveolar':
'#c260ff',#   'T/NK_cycling':
'#b85f1c',#   'TA':
'#5e0b30',# 'Tfh':
'#5e3c55',#   'Tfh_naive':
'#9c53bc',#     'Tnaive/cm_CD4':
'#5ca4ce',#   'Tnaive/cm_CD8':
'#f98261',#     'Treg':
'#e5c510',# 'Treg_IL10':
'#8107ed',#   'Trm_CD4':
'#2844c1',#   'Trm_CD8':
'#1e093f',#   'Trm_Th17':
'#256b87',#   'Trm/em_CD8':
'#9d9dff',#   'Tuft':
'#1E4147',#   'Vascular_smooth_muscle':
'#d64582'#   'Villus_fibroblast_F3':
)
```





```{r fig.height=5,fig.width=5}
netVisual_chord_gene(cellchat, signaling = c("SAA"), legend.pos.x = 8, scale=FALSE,sources.use = c(30,32,56,57,63), targets.use = c(30,32,56,57,63), lab.cex=1,color.use = colours) 
```
```{r}
netVisual_chord_gene <- function(object, slot.name = "net", color.use = NULL,
                                 signaling = NULL, pairLR.use = NULL, net = NULL,
                                 sources.use = NULL, targets.use = NULL,
                                 lab.cex = 0.8,small.gap = 1, big.gap = 10, annotationTrackHeight = c(0.03),
                                 link.visible = TRUE, scale = FALSE, directional = 1, link.target.prop = TRUE, reduce = -1,
                                 transparency = 0.4, link.border = NA,
                                 title.name = NULL, legend.pos.x = 20, legend.pos.y = 20, show.legend = TRUE,
                                 thresh = 0.05,
                                 ...){
  if (!is.null(pairLR.use)) {
    if (!is.data.frame(pairLR.use)) {
      stop("pairLR.use should be a data frame with a signle column named either 'interaction_name' or 'pathway_name' ")
    } else if ("pathway_name" %in% colnames(pairLR.use)) {
      message("slot.name is set to be 'netP' when pairLR.use contains signaling pathways")
      slot.name = "netP"
    }
  }

  if (!is.null(pairLR.use) & !is.null(signaling)) {
    stop("Please do not assign values to 'signaling' when using 'pairLR.use'")
  }

  if (is.null(net)) {
    prob <- slot(object, "net")$prob
    pval <- slot(object, "net")$pval
    prob[pval > thresh] <- 0
    net <- reshape2::melt(prob, value.name = "prob")
    colnames(net)[1:3] <- c("source","target","interaction_name")

    pairLR = dplyr::select(object@LR$LRsig, c("interaction_name_2", "pathway_name",  "ligand",  "receptor" ,"annotation","evidence"))
    idx <- match(net$interaction_name, rownames(pairLR))
    temp <- pairLR[idx,]
    net <- cbind(net, temp)
  }

  if (!is.null(signaling)) {
    pairLR.use <- data.frame()
    for (i in 1:length(signaling)) {
      pairLR.use.i <- searchPair(signaling = signaling[i], pairLR.use = object@LR$LRsig, key = "pathway_name", matching.exact = T, pair.only = T)
      pairLR.use <- rbind(pairLR.use, pairLR.use.i)
    }
  }

  if (!is.null(pairLR.use)){
    if ("interaction_name" %in% colnames(pairLR.use)) {
      net <- subset(net,interaction_name %in% pairLR.use$interaction_name)
    } else if ("pathway_name" %in% colnames(pairLR.use)) {
      net <- subset(net, pathway_name %in% as.character(pairLR.use$pathway_name))
    }
  }

  if (slot.name == "netP") {
    net <- dplyr::select(net, c("source","target","pathway_name","prob"))
    net$source_target <- paste(net$source, net$target, sep = "sourceTotarget")
    net <- net %>% dplyr::group_by(source_target, pathway_name) %>% dplyr::summarize(prob = sum(prob))
    a <- stringr::str_split(net$source_target, "sourceTotarget", simplify = T)
    net$source <- as.character(a[, 1])
    net$target <- as.character(a[, 2])
    net$ligand <- net$pathway_name
    net$receptor <- " "
  }

  # keep the interactions associated with sources and targets of interest
  if (!is.null(sources.use)){
    if (is.numeric(sources.use)) {
      sources.use <- levels(object@idents)[sources.use]
    }
    net <- subset(net, source %in% sources.use)
  } else {
    sources.use <- levels(object@idents)
  }
  if (!is.null(targets.use)){
    if (is.numeric(targets.use)) {
      targets.use <- levels(object@idents)[targets.use]
    }
    net <- subset(net, target %in% targets.use)
  } else {
    targets.use <- levels(object@idents)
  }
  # remove the interactions with zero values
  df <- subset(net, prob > 0)

  if (nrow(df) == 0) {
    stop("No signaling links are inferred! ")
  }

  if (length(unique(net$ligand)) == 1) {
    message("You may try the function `netVisual_chord_cell` for visualizing individual signaling pathway")
  }

  df$id <- 1:nrow(df)
  # deal with duplicated sector names
  ligand.uni <- unique(df$ligand)
  for (i in 1:length(ligand.uni)) {
    df.i <- df[df$ligand == ligand.uni[i], ]
    source.uni <- unique(df.i$source)
    for (j in 1:length(source.uni)) {
      df.i.j <- df.i[df.i$source == source.uni[j], ]
      df.i.j$ligand <- paste0(df.i.j$ligand, paste(rep(' ',j-1),collapse = ''))
      df$ligand[df$id %in% df.i.j$id] <- df.i.j$ligand
    }
  }
  receptor.uni <- unique(df$receptor)
  for (i in 1:length(receptor.uni)) {
    df.i <- df[df$receptor == receptor.uni[i], ]
    target.uni <- unique(df.i$target)
    for (j in 1:length(target.uni)) {
      df.i.j <- df.i[df.i$target == target.uni[j], ]
      df.i.j$receptor <- paste0(df.i.j$receptor, paste(rep(' ',j-1),collapse = ''))
      df$receptor[df$id %in% df.i.j$id] <- df.i.j$receptor
    }
  }

  cell.order.sources <- levels(object@idents)[levels(object@idents) %in% sources.use]
  cell.order.targets <- levels(object@idents)[levels(object@idents) %in% targets.use]

  df$source <- factor(df$source, levels = cell.order.sources)
  df$target <- factor(df$target, levels = cell.order.targets)
  # df.ordered.source <- df[with(df, order(source, target, -prob)), ]
  # df.ordered.target <- df[with(df, order(target, source, -prob)), ]
  df.ordered.source <- df[with(df, order(source, -prob)), ]
  df.ordered.target <- df[with(df, order(target, -prob)), ]

  order.source <- unique(df.ordered.source[ ,c('ligand','source')])
  order.target <- unique(df.ordered.target[ ,c('receptor','target')])

  # define sector order
  order.sector <- c(order.source$ligand, order.target$receptor)

  # define cell type color
  if (is.null(color.use)){
    color.use = scPalette(nlevels(object@idents))
    names(color.use) <- levels(object@idents)
    color.use <- color.use[levels(object@idents) %in% as.character(union(df$source,df$target))]
  } else if (is.null(names(color.use))) {
    names(color.use) <- levels(object@idents)
    color.use <- color.use[levels(object@idents) %in% as.character(union(df$source,df$target))]
  }

  # define edge color
  edge.color <- color.use[as.character(df.ordered.source$source)]
  names(edge.color) <- as.character(df.ordered.source$source)

  # define grid colors
  grid.col.ligand <- color.use[as.character(order.source$source)]
  names(grid.col.ligand) <- as.character(order.source$source)
  grid.col.receptor <- color.use[as.character(order.target$target)]
  names(grid.col.receptor) <- as.character(order.target$target)
  grid.col <- c(as.character(grid.col.ligand), as.character(grid.col.receptor))
  names(grid.col) <- order.sector

  df.plot <- df.ordered.source[ ,c('ligand','receptor','prob')]

  if (directional == 2) {
    link.arr.type = "triangle"
  } else {
    link.arr.type = "big.arrow"
  }
  circos.clear()
  chordDiagram(df.plot,
               order = order.sector,
               col = edge.color,
               grid.col = grid.col,
               transparency = transparency,
               link.border = link.border,
               directional = directional,
               direction.type = c("diffHeight","arrows"),
               link.arr.type = link.arr.type,
               annotationTrack = "grid",
               annotationTrackHeight = annotationTrackHeight,
               preAllocateTracks = list(track.height = max(strwidth(order.sector))),
               small.gap = small.gap,
               big.gap = big.gap,
               link.visible = link.visible,
               scale = scale,
               link.target.prop = link.target.prop,
               reduce = reduce,
               ...)

  circos.track(track.index = 1, panel.fun = function(x, y) {
    xlim = get.cell.meta.data("xlim")
    xplot = get.cell.meta.data("xplot")
    ylim = get.cell.meta.data("ylim")
    sector.name = get.cell.meta.data("sector.index")
    circos.text(mean(xlim), ylim[1], sector.name,facing = "clockwise", niceFacing = TRUE, adj=c(0.5,0.01),cex = lab.cex)
  }, bg.border = NA)


  # https://jokergoo.github.io/circlize_book/book/legends.html
  if (show.legend) {
    lgd <- ComplexHeatmap::Legend(at = names(color.use), type = "grid", legend_gp = grid::gpar(fill = color.use), title = "Cell State")
    ComplexHeatmap::draw(lgd, x = unit(1, "npc")-unit(legend.pos.x, "mm"), y = unit(legend.pos.y, "mm"), just = c("right", "bottom"))
  }

  circos.clear()
  if(!is.null(title.name)){
    text(-0, 1.02, title.name, cex=1)
  }
  gg <- recordPlot()
  return(gg)
}
```

```{r fig.height=15,fig.width=15}
netVisual_chord_gene(cellchat, signaling = c("CXCL"), legend.pos.x = 8, scale=FALSE,sources.use = c(57,63), lab.cex=0.5,color.use = colours) 
```
```{r fig.height=15,fig.width=15}
netVisual_chord_gene(cellchat, signaling = c("CXCL"), legend.pos.x = 8, scale=TRUE,sources.use = c(57,63),targets.use =c(24,29,46,50,60,62,69,70,72,73), lab.cex=0.5,color.use = colours) 
```
```{r fig.height=15,fig.width=15}
pdf(file ="all_genes_plots/cellchat_MUC6OralFibrosource_cxclchord.pdf", width = 15, height =15)
netVisual_chord_gene(cellchat, signaling = c("CXCL"), legend.pos.x = 8, scale=TRUE,sources.use = c(57,63),targets.use =c(24,29,46,50,60,62,69,70,72,73), lab.cex=0.5,color.use = colours) 
```

```{r fig.height=15,fig.width=15}
netVisual_chord_gene(cellchat, signaling = c("CXCL"), legend.pos.x = 8, scale=TRUE,sources.use = c(57), lab.cex=1,color.use = colours) 
```


```{r fig.height=15,fig.width=15}
pdf(file ="all_genes_plots/cellchat_MUC6source_cxclchord.pdf", width = 15, height =15)
netVisual_chord_gene(cellchat, signaling = c("CXCL"), legend.pos.x = 8, scale=TRUE,sources.use = c(57), lab.cex=1,color.use = colours) 
```


```{r fig.height=15,fig.width=15}
netVisual_chord_gene(cellchat, signaling = c("CCL"), legend.pos.x = 8, scale=FALSE,sources.use = c(57,63), lab.cex=0.5,color.use = colours) 
```

```{r fig.height=15,fig.width=15}
netVisual_chord_gene(cellchat, signaling = c("CXCL"), legend.pos.x = 8, scale=FALSE,sources.use = c(57,63), lab.cex=0.5,color.use = colours) 
```


```{r fig.height=15,fig.width=15}
netVisual_chord_gene(cellchat, signaling = c("MHC-II"), legend.pos.x = 8, scale=FALSE,sources.use = c(57), lab.cex=0.5,color.use = colours) 
```


### code from previous notebook
```{r fig.height=15,fig.width=15}
netVisual_chord_gene(cellchat, signaling = c("IL6"),legend.pos.x = 8, scale=FALSE, sources.use = c(1,2,3,4,5,6,7), targets.use = c(1,2,3,4,5,6,7), lab.cex=1,  color.use = c('#CD6600', '#FF8C00', '#CD00CD','#0000EE', '#8FBC8F','#C1FFC1','#228B22')) 
```

```{r}
netVisual_chord_gene(cellchat, signaling = c("CCL"),legend.pos.x = 8, scale=TRUE, sources.use = c(5,6,7), targets.use = c(1,2,3,4,5,6,7), lab.cex=0.5,  color.use = c('#CD6600', '#FF8C00', '#CD00CD', '#0000EE',  '#8FBC8F','#C1FFC1','#228B22')) 

```

```{r}
netVisual_chord_gene(cellchat, signaling = c("IL6"),legend.pos.x = 8, scale=TRUE, sources.use = c(5,6,7), targets.use = c(1,2,3,4), lab.cex=1.5, color.use = c('#CD6600', '#FF8C00', '#CD00CD',  '#4169E1', '#0000EE', '#ADD8E6', '#8FBC8F','#C1FFC1','#228B22','#6495ED')) 

```

```{r}
netVisual_chord_gene(cellchat, signaling = c("IL6"),legend.pos.x = 8, scale=TRUE, lab.cex=1,  color.use = c('#CD6600', '#FF8C00', '#CD00CD','#0000EE', '#8FBC8F','#C1FFC1','#228B22'))
```

Below is just changes to the default circle/chord plots from the source code, the default option is to have the labels sticking out from the plot instead of wrapping around this can be changed in circos.text setting facing (https://www.rdocumentation.org/packages/circlize/versions/0.4.13/topics/circos.text)

```{r fig.height=10,fig.width=10}
#HLA-DRA
pathways.show <- c("MHC-II")
pairLR.MHCII <- extractEnrichedLR(cellchat, signaling = pathways.show, geneLR.return = FALSE)
LR.show <- pairLR.MHCII[11,] #HLA-DRA is 10 and HLA-DRB1 is 11
netVisual_individual(cellchat, signaling = pathways.show, pairLR.use = LR.show, layout = "chord", color.use = c('#CD6600', '#FF8C00', '#CD00CD',  '#4169E1', '#0000EE', '#ADD8E6', '#8FBC8F','#C1FFC1','#228B22','#6495ED'),remove.isolate=TRUE)
```
```{r}
netVisual_individual <- function(object, signaling, signaling.name = NULL, pairLR.use = NULL, color.use = NULL, vertex.receiver = NULL, sources.use = NULL, targets.use = NULL, top = 1, remove.isolate = FALSE,
                                 vertex.weight = 1, vertex.weight.max = NULL, vertex.size.max = NULL, vertex.label.cex = 0.8,
                                 weight.scale = FALSE, edge.weight.max = NULL, edge.width.max=8, graphics.init = TRUE,
                                 layout = c("circle","hierarchy","chord"), height = 5, thresh = 0.05, #from = NULL, to = NULL, bidirection = NULL,vertex.size = NULL,
                                 group = NULL,cell.order = NULL,small.gap = 1, big.gap = 10, scale = FALSE, reduce = -1, show.legend = FALSE, legend.pos.x = 20, legend.pos.y = 20, nCol = NULL,
                                 ...) {
  layout <- match.arg(layout)
  # if (!is.null(vertex.size)) {
  #   warning("'vertex.size' is deprecated. Use `vertex.weight`")
  # }
  if (is.null(vertex.weight)) {
    vertex.weight <- as.numeric(table(object@idents))
  }
  if (is.null(vertex.size.max)) {
    if (length(unique(vertex.weight)) == 1) {
      vertex.size.max <- 5
    } else {
      vertex.size.max <- 15
    }
  }

  pairLR <- searchPair(signaling = signaling, pairLR.use = object@LR$LRsig, key = "pathway_name", matching.exact = T, pair.only = F)

  if (is.null(signaling.name)) {
    signaling.name <- signaling
  }
  net <- object@net

  pairLR.use.name <- dimnames(net$prob)[[3]]
  pairLR.name <- intersect(rownames(pairLR), pairLR.use.name)
  if (!is.null(pairLR.use)) {
    if (is.data.frame(pairLR.use)) {
      pairLR.name <- intersect(pairLR.name, as.character(pairLR.use$interaction_name))
    } else {
      pairLR.name <- intersect(pairLR.name, as.character(pairLR.use))
    }

    if (length(pairLR.name) == 0) {
      stop("There is no significant communication for the input L-R pairs!")
    }
  }

  pairLR <- pairLR[pairLR.name, ]
  prob <- net$prob
  pval <- net$pval

  prob[pval > thresh] <- 0
  if (length(pairLR.name) > 1) {
    pairLR.name.use <- pairLR.name[apply(prob[,,pairLR.name], 3, sum) != 0]
  } else {
    pairLR.name.use <- pairLR.name[sum(prob[,,pairLR.name]) != 0]
  }

  if (length(pairLR.name.use) == 0) {
    stop(paste0('There is no significant communication of ', signaling.name))
  } else {
    pairLR <- pairLR[pairLR.name.use,]
  }

  nRow <- length(pairLR.name.use)

  prob <- prob[,,pairLR.name.use]
  pval <- pval[,,pairLR.name.use]

  if (is.null(nCol)) {
    nCol <- min(length(pairLR.name.use), 2)
  }

  if (length(dim(prob)) == 2) {
    prob <- replicate(1, prob, simplify="array")
    pval <- replicate(1, pval, simplify="array")
  }

 # prob <-(prob-min(prob))/(max(prob)-min(prob))
  if (is.null(edge.weight.max)) {
    edge.weight.max = max(prob)
  }

  if (layout == "hierarchy") {
    if (graphics.init) {
      par(mfrow=c(nRow,2), mar = c(5, 4, 4, 2) +0.1)
    }

    for (i in 1:length(pairLR.name.use)) {
      signalName_i <- pairLR$interaction_name_2[i]
      prob.i <- prob[,,i]
      netVisual_hierarchy1(prob.i, vertex.receiver = vertex.receiver, sources.use = sources.use, targets.use = targets.use, remove.isolate = remove.isolate, top = top, color.use = color.use, vertex.weight = vertex.weight, vertex.weight.max = vertex.weight.max, vertex.size.max = vertex.size.max, weight.scale = weight.scale, edge.weight.max = edge.weight.max, edge.width.max=edge.width.max, title.name = signalName_i,...)
      netVisual_hierarchy2(prob.i, vertex.receiver = setdiff(1:nrow(prob.i),vertex.receiver), sources.use = sources.use, targets.use = targets.use, remove.isolate = remove.isolate, top = top, color.use = color.use, vertex.weight = vertex.weight, vertex.weight.max = vertex.weight.max, vertex.size.max = vertex.size.max, weight.scale = weight.scale, edge.weight.max = edge.weight.max, edge.width.max=edge.width.max, title.name = signalName_i,...)
    }
    # grid.echo()
    # gg <-  grid.grab()
    gg <- recordPlot()

  } else if (layout == "circle") {
   # par(mfrow=c(nRow,1))
    if (graphics.init) {
      par(mfrow = c(ceiling(length(pairLR.name.use)/nCol), nCol), xpd=TRUE)
    }

    gg <- vector("list", length(pairLR.name.use))
    for (i in 1:length(pairLR.name.use)) {
      signalName_i <- pairLR$interaction_name_2[i]
      prob.i <- prob[,,i]
      gg[[i]] <- netVisual_circle(prob.i, sources.use = sources.use, targets.use = targets.use, remove.isolate = remove.isolate, top = top, color.use = color.use, vertex.weight = vertex.weight, vertex.weight.max = vertex.weight.max, vertex.size.max = vertex.size.max, weight.scale = weight.scale, edge.weight.max = edge.weight.max, edge.width.max=edge.width.max, title.name = signalName_i,...)
    }
  } else if (layout == "chord") {
    if (graphics.init) {
      par(mfrow = c(ceiling(length(pairLR.name.use)/nCol), nCol), xpd=TRUE)
    }

    gg <- vector("list", length(pairLR.name.use))
    for (i in 1:length(pairLR.name.use)) {
      title.name <- pairLR$interaction_name_2[i]
      net <- prob[,,i]
      gg[[i]] <- netVisual_chord_cell_internal(net, color.use = color.use, sources.use = sources.use, targets.use = targets.use, remove.isolate = remove.isolate,
                                               group = group, cell.order = cell.order,
                                               lab.cex = vertex.label.cex,small.gap = small.gap, big.gap = big.gap,
                                               scale = scale, reduce = reduce,
                                               title.name = title.name, show.legend = show.legend, legend.pos.x = legend.pos.x, legend.pos.y = legend.pos.y)
    }
  }
  return(gg)
}
```

```{r}
netVisual_chord_cell_internal <- function(net, color.use = NULL, group = NULL, cell.order = NULL,
                                          sources.use = NULL, targets.use = NULL,
                                          lab.cex = 0.8,small.gap = 1, big.gap = 10, annotationTrackHeight = c(0.03),
                                          remove.isolate = FALSE, link.visible = TRUE, scale = FALSE, directional = 1, link.target.prop = TRUE, reduce = -1,
                                          transparency = 0.4, link.border = NA,
                                          title.name = NULL, show.legend = FALSE, legend.pos.x = 20, legend.pos.y = 20,...){
  if (inherits(x = net, what = c("matrix", "Matrix"))) {
    cell.levels <- union(rownames(net), colnames(net))
    net <- reshape2::melt(net, value.name = "prob")
    colnames(net)[1:2] <- c("source","target")
  } else if (is.data.frame(net)) {
    if (all(c("source","target", "prob") %in% colnames(net)) == FALSE) {
      stop("The input data frame must contain three columns named as source, target, prob")
    }
    cell.levels <- as.character(union(net$source,net$target))
  }
  if (!is.null(cell.order)) {
    cell.levels <- cell.order
  }
  net$source <- as.character(net$source)
  net$target <- as.character(net$target)

  # keep the interactions associated with sources and targets of interest
  if (!is.null(sources.use)){
    if (is.numeric(sources.use)) {
      sources.use <- cell.levels[sources.use]
    }
    net <- subset(net, source %in% sources.use)
  }
  if (!is.null(targets.use)){
    if (is.numeric(targets.use)) {
      targets.use <- cell.levels[targets.use]
    }
    net <- subset(net, target %in% targets.use)
  }
  # remove the interactions with zero values
  net <- subset(net, prob > 0)
  # create a fake data if keeping the cell types (i.e., sectors) without any interactions
  if (!remove.isolate) {
    cells.removed <- setdiff(cell.levels, as.character(union(net$source,net$target)))
    if (length(cells.removed) > 0) {
      net.fake <- data.frame(cells.removed, cells.removed, 1e-10*sample(length(cells.removed), length(cells.removed)))
      colnames(net.fake) <- colnames(net)
      net <- rbind(net, net.fake)
      link.visible <- net[, 1:2]
      link.visible$plot <- FALSE
      link.visible$plot[1:(nrow(net) - nrow(net.fake))] <- TRUE
      # directional <- net[, 1:2]
      # directional$plot <- 0
      # directional$plot[1:(nrow(net) - nrow(net.fake))] <- 1
      # link.arr.type = "big.arrow"
      # message("Set scale = TRUE when remove.isolate = FALSE")
      scale = TRUE
    }
  }

  df <- net
  cells.use <- union(df$source,df$target)

  # define grid order
  order.sector <- cell.levels[cell.levels %in% cells.use]

  # define grid color
  if (is.null(color.use)){
    color.use = scPalette(length(cell.levels))
    names(color.use) <- cell.levels
  } else if (is.null(names(color.use))) {
    names(color.use) <- cell.levels
  }
  grid.col <- color.use[order.sector]
  names(grid.col) <- order.sector

  # set grouping information
  if (!is.null(group)) {
    group <- group[names(group) %in% order.sector]
  }

  # define edge color
  edge.color <- color.use[as.character(df$source)]

  if (directional == 0 | directional == 2) {
    link.arr.type = "triangle"
  } else {
    link.arr.type = "big.arrow"
  }

  circos.clear()
  chordDiagram(df,
               order = order.sector,
               col = edge.color,
               grid.col = grid.col,
               transparency = transparency,
               link.border = link.border,
               directional = directional,
               direction.type = c("diffHeight","arrows"),
               link.arr.type = link.arr.type, # link.border = "white",
               annotationTrack = "grid",
               annotationTrackHeight = annotationTrackHeight,
               preAllocateTracks = list(track.height = max(strwidth(order.sector))),
               small.gap = small.gap,
               big.gap = big.gap,
               link.visible = link.visible,
               scale = scale,
               group = group,
               link.target.prop = link.target.prop,
               reduce = reduce,
               ...)
  circos.track(track.index = 1, panel.fun = function(x, y) {
    xlim = get.cell.meta.data("xlim")
    xplot = get.cell.meta.data("xplot")
    ylim = get.cell.meta.data("ylim")
    sector.name = get.cell.meta.data("sector.index")
    circos.text(mean(xlim), ylim[1], sector.name, facing = "bending", niceFacing = TRUE, adj = c(0, 0.5),cex = lab.cex)
  }, bg.border = NA)

  # https://jokergoo.github.io/circlize_book/book/legends.html
  if (show.legend) {
    lgd <- ComplexHeatmap::Legend(at = names(grid.col), type = "grid", legend_gp = grid::gpar(fill = grid.col), title = "Cell State")
    ComplexHeatmap::draw(lgd, x = unit(1, "npc")-unit(legend.pos.x, "mm"), y = unit(legend.pos.y, "mm"), just = c("right", "bottom"))
  }

  if(!is.null(title.name)){
    # title(title.name, cex = 1)
    text(-0, 1.02, title.name, cex=1)
  }
  circos.clear()
  gg <- recordPlot()
  return(gg)
}

```







```{r}
netVisual_chord_gene <- function(object, slot.name = "net", color.use = NULL,
                                 signaling = NULL, pairLR.use = NULL, net = NULL,
                                 sources.use = NULL, targets.use = NULL,
                                 lab.cex = 0.8,small.gap = 1, big.gap = 10, annotationTrackHeight = c(0.03),
                                 link.visible = TRUE, scale = FALSE, directional = 1, link.target.prop = TRUE, reduce = -1,
                                 transparency = 0.4, link.border = NA,
                                 title.name = NULL, legend.pos.x = 20, legend.pos.y = 20, show.legend = TRUE,
                                 thresh = 0.05,
                                 ...){
  if (!is.null(pairLR.use)) {
    if (!is.data.frame(pairLR.use)) {
      stop("pairLR.use should be a data frame with a signle column named either 'interaction_name' or 'pathway_name' ")
    } else if ("pathway_name" %in% colnames(pairLR.use)) {
      message("slot.name is set to be 'netP' when pairLR.use contains signaling pathways")
      slot.name = "netP"
    }
  }

  if (!is.null(pairLR.use) & !is.null(signaling)) {
    stop("Please do not assign values to 'signaling' when using 'pairLR.use'")
  }

  if (is.null(net)) {
    prob <- slot(object, "net")$prob
    pval <- slot(object, "net")$pval
    prob[pval > thresh] <- 0
    net <- reshape2::melt(prob, value.name = "prob")
    colnames(net)[1:3] <- c("source","target","interaction_name")

    pairLR = dplyr::select(object@LR$LRsig, c("interaction_name_2", "pathway_name",  "ligand",  "receptor" ,"annotation","evidence"))
    idx <- match(net$interaction_name, rownames(pairLR))
    temp <- pairLR[idx,]
    net <- cbind(net, temp)
  }

  if (!is.null(signaling)) {
    pairLR.use <- data.frame()
    for (i in 1:length(signaling)) {
      pairLR.use.i <- searchPair(signaling = signaling[i], pairLR.use = object@LR$LRsig, key = "pathway_name", matching.exact = T, pair.only = T)
      pairLR.use <- rbind(pairLR.use, pairLR.use.i)
    }
  }

  if (!is.null(pairLR.use)){
    if ("interaction_name" %in% colnames(pairLR.use)) {
      net <- subset(net,interaction_name %in% pairLR.use$interaction_name)
    } else if ("pathway_name" %in% colnames(pairLR.use)) {
      net <- subset(net, pathway_name %in% as.character(pairLR.use$pathway_name))
    }
  }

  if (slot.name == "netP") {
    net <- dplyr::select(net, c("source","target","pathway_name","prob"))
    net$source_target <- paste(net$source, net$target, sep = "sourceTotarget")
    net <- net %>% dplyr::group_by(source_target, pathway_name) %>% dplyr::summarize(prob = sum(prob))
    a <- stringr::str_split(net$source_target, "sourceTotarget", simplify = T)
    net$source <- as.character(a[, 1])
    net$target <- as.character(a[, 2])
    net$ligand <- net$pathway_name
    net$receptor <- " "
  }

  # keep the interactions associated with sources and targets of interest
  if (!is.null(sources.use)){
    if (is.numeric(sources.use)) {
      sources.use <- levels(object@idents)[sources.use]
    }
    net <- subset(net, source %in% sources.use)
  } else {
    sources.use <- levels(object@idents)
  }
  if (!is.null(targets.use)){
    if (is.numeric(targets.use)) {
      targets.use <- levels(object@idents)[targets.use]
    }
    net <- subset(net, target %in% targets.use)
  } else {
    targets.use <- levels(object@idents)
  }
  # remove the interactions with zero values
  df <- subset(net, prob > 0)

  if (nrow(df) == 0) {
    stop("No signaling links are inferred! ")
  }

  if (length(unique(net$ligand)) == 1) {
    message("You may try the function `netVisual_chord_cell` for visualizing individual signaling pathway")
  }

  df$id <- 1:nrow(df)
  # deal with duplicated sector names
  ligand.uni <- unique(df$ligand)
  for (i in 1:length(ligand.uni)) {
    df.i <- df[df$ligand == ligand.uni[i], ]
    source.uni <- unique(df.i$source)
    for (j in 1:length(source.uni)) {
      df.i.j <- df.i[df.i$source == source.uni[j], ]
      df.i.j$ligand <- paste0(df.i.j$ligand, paste(rep(' ',j-1),collapse = ''))
      df$ligand[df$id %in% df.i.j$id] <- df.i.j$ligand
    }
  }
  receptor.uni <- unique(df$receptor)
  for (i in 1:length(receptor.uni)) {
    df.i <- df[df$receptor == receptor.uni[i], ]
    target.uni <- unique(df.i$target)
    for (j in 1:length(target.uni)) {
      df.i.j <- df.i[df.i$target == target.uni[j], ]
      df.i.j$receptor <- paste0(df.i.j$receptor, paste(rep(' ',j-1),collapse = ''))
      df$receptor[df$id %in% df.i.j$id] <- df.i.j$receptor
    }
  }

  cell.order.sources <- levels(object@idents)[levels(object@idents) %in% sources.use]
  cell.order.targets <- levels(object@idents)[levels(object@idents) %in% targets.use]

  df$source <- factor(df$source, levels = cell.order.sources)
  df$target <- factor(df$target, levels = cell.order.targets)
  # df.ordered.source <- df[with(df, order(source, target, -prob)), ]
  # df.ordered.target <- df[with(df, order(target, source, -prob)), ]
  df.ordered.source <- df[with(df, order(source, -prob)), ]
  df.ordered.target <- df[with(df, order(target, -prob)), ]

  order.source <- unique(df.ordered.source[ ,c('ligand','source')])
  order.target <- unique(df.ordered.target[ ,c('receptor','target')])

  # define sector order
  order.sector <- c(order.source$ligand, order.target$receptor)

  # define cell type color
  if (is.null(color.use)){
    color.use = scPalette(nlevels(object@idents))
    names(color.use) <- levels(object@idents)
    color.use <- color.use[levels(object@idents) %in% as.character(union(df$source,df$target))]
  } else if (is.null(names(color.use))) {
    names(color.use) <- levels(object@idents)
    color.use <- color.use[levels(object@idents) %in% as.character(union(df$source,df$target))]
  }

  # define edge color
  edge.color <- color.use[as.character(df.ordered.source$source)]
  names(edge.color) <- as.character(df.ordered.source$source)

  # define grid colors
  grid.col.ligand <- color.use[as.character(order.source$source)]
  names(grid.col.ligand) <- as.character(order.source$source)
  grid.col.receptor <- color.use[as.character(order.target$target)]
  names(grid.col.receptor) <- as.character(order.target$target)
  grid.col <- c(as.character(grid.col.ligand), as.character(grid.col.receptor))
  names(grid.col) <- order.sector

  df.plot <- df.ordered.source[ ,c('ligand','receptor','prob')]

  if (directional == 2) {
    link.arr.type = "triangle"
  } else {
    link.arr.type = "big.arrow"
  }
  circos.clear()
  chordDiagram(df.plot,
               order = order.sector,
               col = edge.color,
               grid.col = grid.col,
               transparency = transparency,
               link.border = link.border,
               directional = directional,
               direction.type = c("diffHeight","arrows"),
               link.arr.type = link.arr.type,
               annotationTrack = "grid",
               annotationTrackHeight = annotationTrackHeight,
               preAllocateTracks = list(track.height = max(strwidth(order.sector))),
               small.gap = small.gap,
               big.gap = big.gap,
               link.visible = link.visible,
               scale = scale,
               link.target.prop = link.target.prop,
               reduce = reduce,
               ...)

  circos.track(track.index = 1, panel.fun = function(x, y) {
    xlim = get.cell.meta.data("xlim")
    xplot = get.cell.meta.data("xplot")
    ylim = get.cell.meta.data("ylim")
    sector.name = get.cell.meta.data("sector.index")
    circos.text(mean(xlim), ylim[1], sector.name, facing = "bending", niceFacing = TRUE, adj=c(0.5,0.01),cex = lab.cex)
  }, bg.border = NA)


  # https://jokergoo.github.io/circlize_book/book/legends.html
  if (show.legend) {
    lgd <- ComplexHeatmap::Legend(at = names(color.use), type = "grid", legend_gp = grid::gpar(fill = color.use), title = "Cell State")
    ComplexHeatmap::draw(lgd, x = unit(1, "npc")-unit(legend.pos.x, "mm"), y = unit(legend.pos.y, "mm"), just = c("right", "bottom"))
  }

  circos.clear()
  if(!is.null(title.name)){
    text(-0, 1.02, title.name, cex=1)
  }
  gg <- recordPlot()
  return(gg)
}
```


```{r}
netVisual_chord_gene(cellchat, signaling = c("CCL"), 
                     #sources.use = c(1,2,3,5,6,7,8), targets.use = c(1,2,3,5,6,7,8), 
legend.pos.x = 8,scale=TRUE, color.use = c('#CD6600', '#FF8C00', '#CD00CD', #'#388E8E', 
'#80007c', '#8FBC8F', '#C1FFC1', '#228B22'),lab.cex = 0.8) 
```


```{r}
a <- netVisual_chord_gene(cellchat, signaling = c("CCL"), 
                     #sources.use = c(1,2,3,5,6,7,8), targets.use = c(1,2,3,5,6,7,8), 
legend.pos.x = 8,scale=TRUE, color.use = c('#CD6600', '#FF8C00', '#CD00CD', #'#388E8E', 
'#BF8FAF', '#8FBC8F', '#C1FFC1', '#228B22'),lab.cex = 1) 
```



```{r}
jpeg("final_figures/CXCL_chord.jpeg",width=30,height=30,units="cm",quality=100,res=300)
netVisual_chord_gene(cellchat, signaling = c("CXCL"), 
                     #sources.use = c(1,2,3,5,6,7,8), targets.use = c(1,2,3,5,6,7,8), 
legend.pos.x = 8,scale=TRUE, color.use = c('#CD6600', '#FF8C00', '#CD00CD', #'#388E8E', 
'#BC8F8F', '#8FBC8F', '#C1FFC1', '#228B22'),lab.cex = 3) 
dev.off()
```


